International Journal of Biomedical Engineering and Technology (111 papers in press)
An Improved Speckle Noise Reduction Scheme Using Switching and Flagging of Noisy Data for preprocessing of Ultrasonograms in Detection of Down Syndrome during First and Second Trimesters
by Jeba Shiney, Amar Pratap Singh, Priestly Shan
Abstract: Down Syndrome (DS) is reported to be one of the most common chromosomal abnormality, affecting newborns all over the world. Diagnosis of the syndrome at an earlier stage during pregnancy will provide more options for the affected parents to make decisions on the interventional therapies required for the developing child. The techniques which are currently used in diagnosis of DS like amniocentesis and Chorionic Villus Sampling (CVS)are invasive in nature and are associated with some percentage of risk. This paper aims at developing a Clinical Decision Support System (CDSS) for detection of DS from Ultrasound (US) fetal images. As a preliminary step in achieving this, the US images have to be denoised for removal of speckle noise. A Modified Mean Median (MMM) filter has been proposed which is based on the principle of progressive switching theory.Experimental results show that the proposed filter provides better results in terms of Peak Signal to Noise Ratio(PSNR), Image Enhancement Factor(IEF) and so on.
Keywords: Ultrasound; Down Syndrome; Modified Mean Median; Amniocentesis; Chorionic Villus Sampling; Speckle noise; filter; diagnosis; Clinical Decision Support System; Peak Signal to Noise Ratio.
Accurate detection of Dicrotic notch from PPG signal for telemonitoring applications
by Abhishek Chakraborty, Deboleena Sadhukhan, Madhuchhanda Mitra
Abstract: Recent technological advancement have inspired the modern population to adopt a portable, simple personal telemonitoring system that uses easy-to-acquire biosignal such as Photoplethysmogram (PPG) for regular monitoring of vital signs. Consequently computerized analysis of PPG signal through accurate detection of clinically significant PPG fiducial points like dicrotic notch has become a key research area for early detection of physiological anomalies. In this research, a simple and robust algorithm is proposed for accurate detection of dicrotic notch from the PPG signal employing first and second difference of the denoised signal, slope-reversal and an empirical formula-based approach. Features related to the dicrotic notch are then extracted from the baseline-corrected PPG signal and performance of the algorithm is evaluated over different standard PPG databases as well as over originally acquired signal. The algorithm achieves high efficiency in terms of sensitivity, positive predictivity, detection accuracy and low value of errors in the detected features.
Keywords: Photoplethysmogram; amplitude threshold; slope reversal; dicrotic notch detection.
PULSATILE FLOW, MICRO-SCALE ERYTHROCYTE-PLATELET INTERACTION
by Thakir AlMomani, Suleiman Bani Hani, Samer Awad, Mohammad Al-Abed, Hesham AlMomani, Mohammad Ababneh
Abstract: Platelet aggregation, activation, and adhesion on the blood vessel and implants result in the formation of the mural thrombi. Erythrocyte (or red blood cell RBC) have shown to play a significant role in the aggregation process of platelets toward vessel walls. A level-set sharp-interface immersed boundary method is employed in the computations in which RBC and platelet boundaries are advected on a two-dimensional Cartesian co-ordinate grid system. RBCs and platelets are treated as rigid non-deformed particles, where RBC assumed to have an elliptical shape while platelet is assumed to have a discoid shape. Both steady and pulsatile flow regimes were employed with Reynolds number values equivalent to those could find in the micro-blood vessels. Forces and torques between colliding blood cells are modeled using an extension of the soft sphere model for elliptical particles. RBCs and platelets are transported under the forces and torques induced by fluid flow and cell collision based on solving the momentum equation for each blood cell. The computational results indicated that platelets tend to show more interaction with RBCs and migration toward vessel wall for steady flow more than those found in the pulsatile flow. Velocity contours didnt show major differences in the peak and minimal values. The using of physiological flow conditions showed less interaction between RBCs and platelets, than that could find in the steady flow conditions. Moreover, platelets tend to concentrate in the core region in the case of pulsatile flow.
Keywords: Erythrocyte; platelet; interaction; pulsatile flow; migration; core region; wall region.
A Review on Motor Neuron Disabilities and Treatments
by Ankita Tiwari, O.P. Singh, Dinesh Bhatia
Abstract: Neuromotor or Motor Neuron disabilities (MND) are a set of medical conditions that are incurable and come with a bunch of associated problems. The disease affects the individual motor neuron functioning that can be in the whole body or any specific part of the body. The disability could be the result of improper communication between motor neurons and muscle fibre. In this review paper, we study and enumerate different neuromotor disabilities, and related treatments available till date. Although several interventions have been proposed for rehabilitation of such patients, accurate and reliable methods still need to be researched for improvement in the patients condition.
Keywords: Motor Neuron Disease; Rehabilitation; muscle fibre.
ANALYSIS OF BREAST CANCER USING Gray LEVEL CO-OCCURRENCE MATRIX AND RANDOM FOREST CLASSIFIER
by Tamilarasan Ananth Kumar, G. Rajakumar, T.S. Arun Samuel
Abstract: This paper introduces two features of Neighborhood Structural Similarity (NSS) with Gray Level Co-occurrence Matrix (GLCM) are proposed for the feature extraction of mammographic masses and Random Forest (RF) classifier is used for classification whether the extracted masses are benign or malignant. NSS describes the equivalence in the midst of proximate regions of masses by combining two new features NSS-I and NSS-II. Benign masses are analogous and have systematic patterns. Malignant masses contain indiscriminate patterns because of their miscellaneous attributes. For benign-malignant mass classification number of texture features are proposed namely correlation, contrast, energy and homogeneity; It quantifies neighboring pixels relationship and is unable to capture structural similarity within proximate regions. The performance of the features are evaluated using the images from the mini-MIAS and DDSM datasets, the Random Forest classifier does the recognition. This involves proper classification of masses with high accuracy.
Keywords: Neighbourhood Structural Similarity; contrast; energy; homogeneity; correlation; Gray level Co-occurrence Matrix.
Principal and Independent Component Based Analysis to Enhance Adaptive Noise Canceller for Electrocardiogram Signals
by Mangesh Ramaji Kose, Mitul Kumar Ahirwal, Rekh Ram Janghel
Abstract: In this paper, the proposed methodology has suggested a way to fulfill the need of reference signal for adaptive filtering (AF) of electrocardiogram (ECG) signals. ECG signals are most important form of representation and observation of different heart conditions. During recording process the ECG signals gets contaminated with different types of noises like, baseline wander (BW), electrode motion artifact (MA), muscle noise also known as electromyogram (EMG). Noise contamination causes distortion of normal structure of ECG signal. Adaptive filters works fine for ECG noise cancellation. But, the problem is the need of reference signal or estimation of noise signal. To solve this problem principal and Independent component (PCA and ICA) of noisy signal has been analyzed to extract the noise signal, which is used in adaptive noise cancellation of ECG signals. Fidelity parameters like Mean Square Error (MSE), Signal to Noise ratio (SNR) and Maximum Error (ME) has been observed to measure the quality of filtered signals.
Keywords: PCA; ICA; Adaptive Filters; ECG; Artifacts.
Breast Cancer Image Enhancement with the Aid of Optimum Wavelet-Based Image Enhancement using Social Spider Optimization
by T. Venkata Satya Vivek, C. Raju, D. Girish Kumar
Abstract: This paper is to enhance features and gain higher traits of breast cancer images utilizing Optimum Wavelet-Based Image Enhancement (OWBIE) with Social Spider Optimization (SSO). More than a few biomedical images are of low quality and difficulty to detect and exact information. The converted gray pictures are utilized for filtering approach; here optimum Wavelet-Based Image Enhancement (OWBIE) with Social Spider Optimization (SSO), Histogram equalization, Anti-forensic distinction enhancement process and Curvelet centered distinction enhancement are used. The proposed technique is used to remove noise and hold area moderately sharp in the fed enter images. In the result, more than a few Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) evaluation metrics graphs are analyzed and more enhanced. This proposed process performed better in comparison with different enhancement techniques.
Keywords: Optimum Wavelet-Based Image Enhancement (OWBIE); Social Spider Optimization (SSO); Breast Cancer.
LUNG CANCER DIAGNOSIS AND STAGING USING FIREFLY ALGORITHM FUZZY C-MEANS SEGMENTATION AND SUPPORT VECTOR MACHINE CLASSIFICATION OF LUNG NODULES
Abstract: Lung Nodule segmentation is an important division of automated disease screening systems in cancer identification. The morphological variations of lung nodules correspond to chances of cancer. The incorrect detection of these lung nodules because of misclassification leads to false results and incorrect strategies of diagnosis. This misclassification also misdirects pharmaceutical experts for wrong preparation of drugs for diagnosis. There are different methods that are available for detection but always there is a space for improvement in terms of various parameters for better results. Therefore in this work image enhancement is done by histogram equalization and further noise removal is carried over by anisotropic diffusion filter. The nodule segmentation process is carried over by Firefly Algorithm Fuzzy C-Means (FA-FCM) segmentation process. Finally, after feature extraction is done classification of lung cancer staging is carried out using Support Vector Machine (SVM) classifier. Therefore, the nodule is accurately detected considering the morphological changes that are noted for the results which lead to proper medicine preparation and accurate diagnosis of lung nodules.
Keywords: Lung Nodule; histogram equalization; anisotropic filter; segmentation; FAFCM; SVM.
Performance Analysis of Preprocessing Filters Using Computed Tomography Images for Liver Lesion Diagnosis
by SHANILA NAZEERA, VINOD KUMAR R.S, RAMYA RAVI R
Abstract: In medical research, segmentation can be used in separating different
tissues from each other, through extracting and classifying features. This paper
aims to discuss the need, concept and advantages of preprocessing techniques
normally used for enhancing the scanned images before segmentation. In addition,
three preprocessing filters, which are used to remove the artifacts from the scanned
images are also implemented and intended to analyse the problems which were
developed during the simulation of liver preprocessing methods. Of the three
methods, the curvature anisotropic diffusion filters performed better than the
other filters and the obtained results are satisfactory. Finally a detailed analysis
of parameter selection for Curvature anisotropic diffusion filtering in Computed
tomography images is performed.
Keywords: Image Processing; Liver Segmentation; MRI; Computed Tomography; Preprocessing; Curvature Anisotropic Diffusion Filter; Noise Removal.
Need for customization in preventing pressure ulcers for wheelchair patients a load distribution approach
by Sivasankar Arumugam, Rajesh Ranganathan, T. Ravi
Abstract: Pressure ulcer (PU) is a healthcare problem developed due to the factors such as pressure, shear and friction. The causes, stages, treatment methods along with mechanical factors and prevention methods for PUs are identified and analyzed. A survey undergone revealed that as people in wheelchairs have different weights and sitting posture thereby, the pressure distribution varies from patient to patient. Therefore, using one type of product for all is found to be inappropriate. In this work, 22 healthy subjects are selected for analyzing the pressure distribution. EMED sensor platform is used for measuring the interface pressure distribution, surface area and peak pressure distribution. From the results it was found that mostly the pressure distribution points for each individual varies drastically. Hence, the need is for individual customization for PU reduction to reduce shear and frictional forces. Here surface customization is identified to be a novel approach for patients in wheelchair.
Keywords: Pressure Ulcer; Customization; Cushions; Wheelchair patients; Load distribution; Surface customization.
An Optimized Clustering Algorithm With Dual Tree Ds For Lossless Image Compression
by Ruhiat Sultana, Nisar Ahmed, Syed Abdul Sattar
Abstract: The emerging utilization of web and other electronic applications have expedited much consideration on image compression systems to spare storage room and diminish transmission time by compressing the size of an image by discarding the repetitive data sequences. Most of the techniques are based on lossy compression techniques where compression ratio would be low. The proposed system based on lossless compression technique achieves best compression ratio, good image quality and less psnr value by extracting the best features of an image which is to be compressed and encoded by incorporating firefly algorithm with k means algorithm which avoids local optima problem. To make the eminent compression of best derived features quad tree decomposition and Huffman encoding technique is combined which provides high compression ratio by fetching correct probabilities of occurrence of pixel intensity. This proposed technique is actualized in MATLAB and in this manner the trial results demonstrated the effectiveness of the proposed image compression technique regarding high compression ratio, low noise ratio and reduced compression and decompression time when compared with existing techniques.
Keywords: Medical imaging; Information systems; Signal processing; hybrid firefly Clustering algorithm; Utilization of quad-tree.
A Comparative study of Feature Projection and Feature Selection approaches for Parkinson's disease detection and classification using T1-weighted MRI scans
by Gunjan Pahuja, T.N. Nagabhushan, Bhanu Prasad
Abstract: In this research, a multivariate analysis between feature projection and feature subset selection methods has been performed with the objective of identifying a subset of features that would help in detection and classification of people affected by Parkinsons disease. For this study, T1-weighted MRI data has been collected from Parkinson's Progression Markers Initiative (PPMI) organization. The accuracy of Support Vector Machine classifier has been checked with different number of selected features during the exploratory phase. The obtained results have shown a clear potential for using these methods in detecting and classifying the Parkinsons patients from normal persons. Further, to identify the brain region responsible for this disease, these selected features are mapped back to the standard MNI brain template. ANOVA test has been employed to show the statistical significance of the obtained results.
Keywords: Parkinson’s disease (PD); Voxel-based morphometry (VBM); Genetic Algorithm (GA); Eigenvector Centrality based discriminant analysis (ECDA); Support Vector Machine (SVM); Analysis of Variance (ANOVA).
e-Health Relationships Diabetes; 50 Weeks Evaluation
by Luuk Simons, Hanno Pijl, John Verhoef, Hildo Lamb, Ben Van Ommen, Bas Gerritsen, Maurice Bizino, Marieke Snel, Ralph Feenstra, Catholijn Jonker
Abstract: Hybrid eHealth support was given to 11 insulin-dependent Type 2 Diabetes Mellites (DM2) patients, with electronic support plus a multi-disciplinary health support team. Challenges were: low ICT- and health literacy. After 50 weeks, attractiveness and feasibility of the intervention were perceived as high: recommendation 9,5 out of 10 and satisfaction 9,6 out of 10. TAM surveys (Technology Acceptance Model) showed high usefulness and feasibility. Acceptance and health behaviours were reinforced by the prolonged health results: Aerobic and strength capacity levels were improved at 50 weeks, plus Health Related Quality of Life (and biometric benefits and medication reductions, reported elsewhere). Regarding eHealth theory, we conclude that iterative skill growth cycles are beneficial for long term adoption and e-relationships. Next, the design analysis shows opportunities for additional affective and social support, on top of the strong benefits already apparent from the direct progress feedback loops used within the health coach processes.
Keywords: Type 2 Diabetes (DM2); eHealth; Lifestyle; Monitoring; Coaching; Blended Care; Service Design.
A method for the classification of mammograms using a statistical based feature extraction
by Nebi Gedik
Abstract: This paper represents a classification system for mammograms using wave atom transform and feature selection process with t-test statistics. Mammogram images are transformed to the wave atom coefficients using wave atom transform. Next, a matrix is constructed from the coefficients. The matrix is used as the feature matrix in order to classify mammograms. To achieve the maximum classification accuracy rate, t-test statistics with a dynamic thresholding is additionally carried out. As a classifier, support vector machine is employed in the classification phase. According to the experimental results, the method proposed in this paper provides a successful contribution for the classification of mammographic images.
Keywords: Mammogram; Classification; Feature extraction; Feature selection; Thresholding; t-test statistics; Wave atom transform; SVM; Normal-abnormal classification; Benign-malignant classification.
A Methodological Review on Computer Aided Diagnosis of Glaucoma in Fundus Images
by Sumaiya Pathan, Preetham Kumar, Radhika M. Pai
Abstract: Advances in computerized image analysis and retinal imaging modalities have significantly contributed towards the growth of image based diagnosis. Glaucoma is an ocular disorder which results in irreversible vision loss. The progression of glaucoma is quiet and in early stages doesnt show any symptoms. Vision loss due to glaucoma has been significantly increasing compared to other retinal disorders. The reliability in diagnosis of glaucoma is limited to the experience and domain knowledge of the ophthalmologist. A computer based diagnostic system can be developed using image processing algorithms for screening large population at less cost, reducing human errors and thus making the diagnosis more objective. A review of the state-of-art-methodologies employed for developing a computer aided diagnosis of glaucoma using retinal fundus image is addressed in this paper along with the future trends.
Keywords: Classification; Cup-to-Disk Ratio; Glaucoma; Optic Disk; Optic Cup; Segmentation.
Automated Melanoma Skin Cancer Detection from Digital Images
by Shalu , RAJNEESH RANI, Aman Kamboj
Abstract: In the early stages, diagnosis of melanoma is important for treating the illness and saving lives. This paper focuses on the development of a system for automatic detection of melanoma skin cancer. The objective of this study is to identify the importance of different colour spaces in melanoma skin cancer detection. Another objective is to compare the colour feature and texture feature to find out that which type of features have more discriminative power to correctly identify melanoma. The whole analysis is done by using the MEDNODE dataset of digital images. This dataset contains a total of 170 images (100 nevi and 70 melanoma). The results show that the combination of features extracted from the HSV (Hue, Saturation and Value) and YCbCr (Y is Luma component and Cb and Cr are two Chroma components) colour space give better performance than the features extracted from other colour spaces. Also, the performance of the system is enhanced with the colour features than the performance with texture features. By using features extracted from the HSV and YCbCr colour space the system shows more accurate result by giving an accuracy of 84.11% which is higher than the earlier approaches on this dataset.
Keywords: Malignant Melanoma; Skin Cancer Diagnosis; Color and Texture Features.
Segmentation of Cartilage in Knee Magnetic Resonance Images using Gabor and Matched Filter and Classification of Osteoarthritis using Adaptive Neuro-Fuzzy Inference System
by Jayashree Palanisamy, Ragupathy Uthandipalayam Subramaniyam
Abstract: Osteoarthritis (OA) also known as degenerative arthritis is a group of mechanical abnormalities occurring in the joints like knee, finger and hip regions. Knee OA is believed to be highly prevalent today because of aging and obesity. Knee region contains complex objects, which varies in appearance significantly from one image to another. Measuring or detecting the presence of particular structures in such images can be a daunting task, since there will be variation in each image.
OA in knee image can be identified by segmenting the bone and cartilage. Finding the region of interest between bone and fat tissue is difficult. Manual and some semiautomatic segmentation methods are time consuming and complex. This can be overcome by the proposed methodology. A method is described here for classification of OA in knee Magnetic Resonance Images (MRI) which deals with segmentation of cartilage region from femur and tibia bone. The images are pre - processed using contrast enhancement technique and Contrast Limited Adaptive Histogram Equalization (CLAHE) and further processed using Matched and Gabor filter for clear recognition of cartilage from background. The noises present are further eliminated using Median filter. Using Gray Level Co-occurrence Matrix (GLCM), features are extracted and extracted features are used for classification of OA. Adaptive Neuro Fuzzy Inference System (ANFIS) classifier is used for classification purpose. The datasets are obtained from Osteoarthritis Initiative (OAI) database and Ganga hospital, Coimbatore.
Keywords: Osteoarthritis; MRI; Gabor filter; Matched filter; CLAHE; Grey Level Co-occurrence Matrix (GLCM); Adaptive Neuro Fuzzy Inference System (ANFIS).
Gustatory Stimulus Based ElectroEncephaloGram (EEG) Signal Classification
by Kalyana Sundaram Chandran, Marichamy Perumalsamy
Abstract: Brain Computer Interface (BCI) gives a prompt correspondence between human brain and Personal Computer (PC). BCI obtains signals from the brain and makes an interpretation for controlling the outside gadgets. Taste Composition (TASCO) based EEG signal classification is used to differentiate normogeusia and hypogeusia. Since an Electroencephalography (EEG) signal is non-stationary and time-changing, features can be extracted either in time domain or frequency domain. The proposed method is mainly used to identify the problems in human organs by using TASCO. EEG signal of TASCO is preprocessed utilizing FIR band pass channel to mitigate the artifacts of noise. In this proposed work, the Discrete Wavelet Transform (DWT) is used as feature extraction method. DWT gives both time and frequency domain representation. DWT breaks down the separated EEG signal into its related frequency bands and the measurable features of the detailed coefficient of the alpha wave are analyzed in time domain. In this proposed method the Mean Absolute Value (MAV) which is an average of the absolute value of the EEG signal and variance of the signal are considered as statistical features. The extracted features are classified using multilayer perceptron neural network classifier which provides high accuracy classification. In this paper, sour TASCO is analyzed to identify the Gall Bladder problem in human organ. The Proposed Method improves the accuracy and performance of the system as much as 95% which cannot be achieved by conventional methods.
Keywords: BCI; Discrete Wavelet Transform; EEG; FIR Band pass filter; gustatory stimuli; MLP; Taste Composition.
MRI Brain Image Volume Property based Accelerate Medical Image Algorithms using CUDA Supported GPU Machine
by Sriramakrishnan Pathmanaban
Abstract: This paper elaborates the design and implementation details of parallel image processing techniques that are used to accelerate the medical image algorithms with CUDA supported GPU. The algorithms are chosen from denoising, morphology, clustering, and segmentation. Three parallel computing models are proposed based on properties of algorithms and MRI volume. The acceleration in parallel algorithms is compared with that of sequential CPU implementation measured in terms of speedup folds (
Keywords: Graphics processing units; Compute unified device architecture; Parallel processing; Medical imaging; Brain volume; Per-pixel threading; Per-slice threading; Hybrid threading; Bilateral filter; Non-local means; K-means clustering; Fuzzy-c-means clustering.
Steady State- VEP based BCI to Control 5 Digit Robotic Hand Using LabVIEW
by Sandesh R S, Nithya Venkatesan
Abstract: This paper proposes Steady State Visual Evoked Potential signals for control of five digit robotic hand using LabVIEW as software platform. The experimental setup consist of Ag/Agcl electrodes with 10-20 gel, a low cost, rechargeable battery operated EEG amplifier, a handmade simulation panel flickering at a frequency of 21 Hz with Light Emitting Diode as source, LabVIEW as software platform to implement wavelet analysis for feature extraction and linear Discriminant analysis for classification and NI USB-DAQ to provide an interface between EEG acquisition and robotic hand. A State machine chart algorithm using PWM technique is implemented for speed control of miniature metal gear DC motor with 71 RPM, positioned in robotic hand. The experiment was carried out for five different subjects with each subject undergoing five trials of which in each trial subject undergoes two recordings of SSVEP signals. Experimental results indicate that the subjects SSVEP signals were used to control the robotic hand to pick up a woolen ball in achieving an accuracy of 84% and mean time of 44.6 seconds. The obtained experimental results were compared against the results obtained from similar works.
Keywords: SSVEP signals; EEG amplifier; LabVIEW; NI-USB DAQ; Robotic Hand; Wavelet analysis; Linear Discriminant Analysis.
Adaptive Fractional Order Controller with Smith Predictor based Propofol Dosing in Intravenous Anaesthesia Automation
by Bhavina Patel
Abstract: This paper is designed to propose a clinical simulation model for automatic propofol dose delivery. We suggest Adaptive Fractional Order Controller (AFCSP) with Smith Predictor design based on CRONE (Commande Robuste dOrdre Non Entier) principle to provide adequate hypnotic intravenous drug infusion regimen for propofol. The main aim of proposed design is to avoid frequent adaption complexity and provide another approach of adaption based on sensitivity parameters in place of BIS signal. Proposed controller is designed from model based analytical method with a two-time domain tuning parameters using explicit equations instead of complex nonlinear equations but yield the same results. AFCSP utilizes the combination of bolus and continuous dose. Robustness test of AFCSP is carried out with patients variability, time delays, surgical stimuli and compared with conventional methods. This scheme is advantageous in terms of improving speed of response, oscillations and overshoots in BIS, also examined on real dataset of 31 different patients.
Keywords: Adaptive Fractional Order Controller with Smith Predictor Controller; Depth of Anaesthesia; Intravenous; BIS; Propofol.
Quantitative Evaluation of Denoising Techniques of Lung Computed Tomography Images: An Experimental Investigation
by Bikesh Kumar Singh
Abstract: Appropriate selection of denoising method is critical component of lung computed tomography (CT) based computer aided diagnosis (CAD) systems, since; noises and artifacts may deteriorate the image quality significantly thereby leading to incorrect diagnosis. This study presents a comparative investigation of various techniques used for denoising lung CT images. Current practices, evaluation measures, research gaps and future challenges in this area are also discussed. Experiments on 20 real-time lung CT images indicate that Gaussian filter with 3
Keywords: Image denoising; lung computed tomography; computer aided diagnosis; image smoothening; edge preservation; quantitative evaluation; image contrast; picture signal to noise ratio; image quality; noise attenuation; time domain and frequency domain.
Real-Time Epileptic Detection from EEG Signals using Statistical Features Optimization and Neural Networks Classification
by Badreddine Mandhouj, Sami Bouzaiane, Mouhamed Ali Cherni, Ines Ben Abdelaziz, Slim Yacoub, Mounir Sayadi
Abstract: This paper describes a completely automated approach in order to enhance the diagnosis of epilepsy disease which is one of the most prevalent neurological disorder. The major aim of this work is to be a potential contribution to the domain. The present paper is divided into three main parts. In the first part, we optimize the statistical features extracted from the EEG signals by a characterization degree. Then, these features are applied to a multilayer neural network (MNN) classifier. In the third part, we use a Digital Signal Peripheral Interface Controller (dsPIC) for the implementation of the real time EEG classification process. The used EEG data are taken from the publicly available database of the University of Bonn and are classified into healthy and epileptic subjects. To assess the performance of this classification method, several performance measures (sensitivity, specificity and accuracy) have been evaluated and have provided interesting results.
Keywords: Electroencephalogram; Epilepsy; Statistical Features; classification; Characterization degree; Optimization; Multilayer neural network; Real-Time; dsPIC.
The evaluation of the healing process of diabetic foot wounds using image segmentation and neural networks classification
by Bruno Da Costa Motta, Marina Pinheiro Marques, Guilherme Dos Anjos Guimarães, Renan Utida Ferreira, Suélia De Siqueira Rodrigues Fleury Rosa
Abstract: Objective: The Diabetic Foot is characterized as an infection or ulceration of the lower limbs tissues. Furthermore, to hinder the evolution of this disease, patients need to be monitored and the evolution as well as the healing processes of the ulcers must be documented. Method: In this regard, this paper proposes the development of an easy-to-use computer program that performs segmentation of ulcers based on the color of the scar tissue and automatically classifies them into three classes by using an artificial neural network, in order to help and ease the diagnosis given by health professionals. Result: The total area of the ulcer, color characteristics of the scar tissue and dimensions of the ulcer can be used as parameters in the diagnosis. Conclusion: The technique developed detected and computed the area of the ulcers, using an imaging protocol, facilitating the application of the technique at hospitals and care units.
Keywords: Wound healing; Medical informatics; Diabetic foot; Image processing; Neural Network.
Mental task classification using wavelet transform and support vector machine
by PRAVIN KSHIRSAGAR
Abstract: The present research on various mental tasks experiencing on human cognitive function disorders using Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM).The Electroencephalogram (EEG) database obtained from online Brain Computer Interface (BCI) Competition paradigm III & offline BAlert EEG Machine from CARE Hospital, Nagpur. EEG signals from paralyzed patients decomposed into the frequency sub-bands using DWT and a set of statistical features extracted from the sub-bands represent the distribution of wavelet coefficients used to reduce the dimension of data, features applied to SVM for classification of left hand and right hand movement. With this system, classification of EEG signals has done with accuracy 91.66% for BCI Competition paradigm III and 97% for B-Alert Machine.
Keywords: BCI; Brain Computer Interface; EEG; Electroencephalogram; Mental Task; DWT; Discrete Wavelet Transform; B- Alert Machine; Classification; SVM; Support Vector Machine; Accuracy; Error; ANN; Artificial Neural Network.
Optimization of Data-set for Classification of Diabetic Retinopathy using Support Vector Machine with Minimal Processing
by Amol Golwankar, Pranav Pailkar, Purvika Patil, Rajendra Sutar
Abstract: Diabetic Retinopathy is a disease observed in the retinal region is caused by a reduced level of insulin in a body or when the pancreas cannot properly process it. If the disease is not recognized in time it may cause permanent blindness. This paper illustrates an optimized approach towards developing a classifier that helps in diagnosing the disease and helps in checking its severity. Using large dataset of 1900 retinal photographs obtained from Kaggle Diabetic Retinopathy Detection Dataset. The proposed classifier classifies the retinal pictures based on the relevant feature values calculated from extracted primary features from the pre-processed and raw images. Classification is performed by support vector machine algorithm that classifies the retinal images into stages or categories such as normal image with no signs of retinopathy, image with mild retinopathy, image with moderate retinopathy, image with severe retinopathy and image showing proliferation of blood vessels respectively with the accuracy of 91.2 percentages.
Keywords: KeywordsDiabetic Retinopathy; Retinal images; Pre-processing; Feature extraction; Machine learning; Support Vector Machine.
A Topological Approach for Mammographic Density Classification Using a Modified Synthetic Minority Over-Sampling Technique Algorithm
by Imane NEDJAR, Said MAHMOUDI, Mohamed Amine Chikh
Abstract: Mammographic density is known to be a risk indicator for breast abnormalities development. Therefore, the breast tissue classification is an important part used in computer aided diagnosis (CAD) system to detect the cancer. In this paper, a CAD system for breast tissue classification using an equilibrating approach is proposed. The first contribution of this paper consists of using a representation of textons distribution by a topological map. This approach allows a good mammographic density classification using the distribution of breast tissue. The second contribution of this work consists of the equilibration of the dataset in the CAD system. Indeed, an improvement of the Synthetic Minority Over-Sampling Technique (SMOTE) algorithm is developed. Our experiments are carried out with MIAS and DDSM datasets to validate the CAD system and two different datasets to validate the proposed modified SMOTE algorithm. The obtained results confirm the validity of the presented proposal.
Keywords: breast tissue classification; SMOTE; textons; computer aided diagnosis systems; mammography; parenchymal patterns; feature extraction; BI-RADS; classification; imbalanced data sets.
Wavelet-based Imagined Speech Classification Using Electroencephalography
by Dipti Pawar, Sudhir Dhage
Abstract: Introduction: Oral communication is the natural way in which humans interact. However, in some circumstances, it is not possible to emit an intelligible acoustic signal, or it is desired to communicate without making sounds. In these conditions systems that enable spoken communication in the absence of an acoustic signal is desirable. In this context, Brain-Computer Interface (BCI) is a remarkable way of solving daily life problems.
Objective: The major objective of the proposed research is to develop an imagined speech classification system based on Electroencephalography
(EEG). The research analysis in the field shows that there is an association between the recorded EEG data and production of speech. We wish to analyse if this can be further true for Imaginary speech.
Approach: We propose an Imagined speech recognition system consists of preprocessing, feature extraction and classification. In the preprocessing stage, EOG artefacts are removed via independent component analysis (ICA). Discrete wavelet transform (DWT) is used to extract Wavelet-based features from EEG segments. Finally, the support vector machine (SVM) is employed for the discriminant of extracted features.
Main Results: The proposed research achieves promising ends up in classification accuracy compared with some of the most common classification techniques in BCI.
Significance: Resultant role indicate significant potential for the utilization of a speech prosthesis controller for clinical and military applications.
Keywords: Electroencephalography; Brain-Computer Interface; DWT; Imagined Speech; SVM.
Numerical Assessment of a 3-D Human Upper Respiratory Tract Model: Effect of Anatomical Structure on Asymmetric Tidal Pulmonary Ventilation Characteristics
by Digamber Singh, Anuj Jain, Akshoy Ranjan Paul
Abstract: The analysis of airway ventilation characteristic is important for diagnosis and pathological assistance for respiratory diseases. It is therefore imperative to study the impact of anatomical features on the internal flow field. The article is focused on an in-silico study on impact of anatomical structure of human upper respiratory tract on transient asymmetric tidal pulmonary ventilation characteristics. Therefore a three-dimensional human airways model is reconstructed from nasal cavity up to 7th generation bronchi from computed Tomography (CT) images of a 48 years healthy man using computational modelling technique. A validated low Reynolds number (LRN) Realizable k-ε turbulence model is used to capture the internal flow mixed turbulence characteristics. The numerical simulations were performed for asymmetric low and high tidal pulmonary ventilation (ALTPV, 10 L/min and AHTPV, 40 L/min). The numerical analysis assists to predict the near realistic airway ventilation phenomena and internal flow physics in the upper respiratory tract.
Keywords: Human upper respiratory tract (HURT); Asymmetric low tidal pulmonary ventilation (ALTPV); Asymmetric high tidal pulmonary ventilation (AHTPV); Computed tomography (CT); Computational fluid dynamics (CFD); Transient; Wall shear stress (WSS); LRN k- ε turbulence model.
Automated Segmentation and Classification of Nuclei in Histopathological Images
by Sanjay Vincent, Chandra J
Abstract: Various kinds of cancer are detected and diagnosed using
histopathological analysis. Recent advances in whole slide scanner technology
and the shift towards digitisation of whole slides have inspired the application of
computational methods on histological data. Digital analysis of histopathological
images has the potential to tackle issues accompanying conventional histological
techniques, like the lack of objectivity and high variability. In this paper, we
present a framework for the automated segmentation of nuclei from human
histopathological whole slide images, and their classification using morphological
and colour characteristics of the nuclei. The segmentation stage consists of two
methods, thresholding and thewatershed transform. The features of the segmented
regions are recorded for the classification stage. Experimental results show that
the knowledge from the selected features is capable of classifying a segmented
object as a candidate nucleus and filtering out the incorrectly identified segments.
Keywords: Histopathological Images; Whole Slide Images; Digital Image Analysis; Segmentation; Nuclei; Annotated; Nuclear; Computer-Assisted Diagnosis; Machine Learning; Classifier; Deep Learning;.
Comparison of Variational Mode Decomposition and Empirical Wavelet Transform methods on EEG signals for Motor Imaginary applications
by Keerthi Krishnan K, Soman K P
Abstract: Devising a reliable method for implementing Brain computer interface (BCI) systems using electroencephalogram (EEG) signals is proposed. Applicability of two modal decomposition methods, Variational Mode Decomposition (VMD) and Empirical Wavelet Transform (EWT) on EEG signals for identifying the four different motor imaginary movements by the investigation of Event-Related Desynchronisation (ERD) activity in the Mu-beta rhythm of EEG signals is analysed and compared. The EEG signals from each electrode corresponding to the sensorimotor cortex area of the brain are decomposed using VMD and EWT methods. Each decomposed modes are modelled using Auto Regressive (AR) modeling and feature vector is formed using the AR model parameters. On classification, better accuracy is perceived for VMD method in comparison with EWT and Common Spatial Pattern (CSP) methods developed on the same data set.
Keywords: VMD; EWT; EEG; SMR; Event-Related Desynchronisation; Motor Imaginary-BCI; BCI competition data set IIIa; Short Time Fourier Transform; AR model; libSVM classifier; Neural network classifier.
Power Line Interference Cancellation from ECG Using Proportionate Normalized Least Mean Square Sub band Adaptive Algorithms
by B. BHASKARA RAO, B. PRABHAKARA RAO
Abstract: The Electrocardiogram (ECG) record is a procedural electrical endeavor of the coronary heart, which is noninvasive recording during which noise such as power-line interference (PLI) with 60 Hz frequency is obtained from power lines. To efficaciously correct and to keep greater underlying components of an ECG signal, a powerful tool for a removal of PLI from a range of signals was introduced earlier. In this research a multiband structured sub band adaptive ﬁlter (MSAF) is developed to clear up structured problems in conventional sub band adaptive ﬁlters. This paper investigates the detailed adaptive noise canceller (ANC) for ECG signals with robustness based on a multi-level decomposition need to be carried out on the noisy signal and then splitting into low sub-bands and high band sub-bands that are performed with the help of uniform filter banks (UFB) and non uniform filter banks (NUFB) structured MSAF using Proportionate NLMS (PNLMS) & Improved Proportionate NLMS (IPNLMS) algorithms. Computer simulation demonstrates that the proposed design gives elevated performance and achieves correct adaption.
Keywords: ECG; IPNLMS; NUFB; UFB; MSAF; SAF.
Biotechnical System and fuzzy logic Models for Prediction and Prevention of Post-Traumatic Inflammatory Complications in Patients with Closed Renal Trauma
by Riad Taha Al-kasasbeh, Nikolay Korenevskiy, Stanislav Petrovich Seregin, Marina Sergeevna Chernega, Altyn Amanzholovna Aikeyeva, Maksim Ilyash
Abstract: Fuzzy logic approach is developed and trained to predict occurance of health implications in blunt kidneys patients. Fuzzy logic is selected because it merges expert judgement with real patients data analysis. A fuzzy decision rules system for forecasting the posttraumatic inflammatory complications of patients with blunt kidneys injury according to the medical and laboratory testing of the research. The research shows high level of lipid peroxidation and antioxidant activity. The research predicts occurance of complications and physician can describe prevention and treatment, combining physical therapy treatments with antioxidant and detoxification therapy.
Keywords: closed injury of the kidney; prognosis; prevention; fuzzy mathematical model.
CBIR BASED DIAGNOSIS OF DERMATOLOGY
by WISELIN JIJI, Rajesh A, Johnson DuraiRaj P
Abstract: In this work, we have presented a computer aided diagnosis approach to assist the diagnosis process of dermatological diseases. The proposed framework is used to retrieve the images from skin lesions repository which in turn facilitates the dermatologist during the diagnosis process. The system used Eigen Disease spaces of respective diseases to converge the search space more efficiently. The results proved using Receiver Operating Characteristic (ROC) curve that the proposed architecture has high contribute to computer-aided diagnosis of skin lesions. Experiments on a set of 1210 images yielded a specificity of 98.44 % and a sensitivity of 86 %. Our empirical evaluation has a superior retrieval and diagnosis performance when compared to the performance of other recent works.
Keywords: Eigen Space; RETRIEVAL SYSTEM,Border Detection.
Automatic Method Recognition of Ischemic Stroke Area on unenhanced CT Brain Images
by Amina Fatima Zahra Yahiaoui, Abdelhafid Bessaid
Abstract: The purpose of this study was to develop a novel automatic method for detection area of subtle hypodensity change of ischemia on unenhanced CT images using comparison of brain hemispheres. Alberta Stroke Program Early CT Score (ASPECTS) has been proposed to help radiologists to make decisions regarding thrombolytic treatment. Only patients with favorable baseline scans (ASPECTS, 810) benefitted from endovascular revascularization therapy. The classification of the images into normal and abnormal depends on the features of left and right side of brain sides. For an accurately detection, we integrated an automatic Midline estimation algorithm to trace it correctly. The proposed method has five steps: preprocessing, segmentation of 10 Regions of Interest (ROIs), elimination of old infarcts and cerebrospinal fluid (CSF) space and feature extraction. The features obtained from ten ROIs were then used to select the abnormal regions and to compute the corresponding ASPECTS score. The method was applied to 50 patients with infarctions of Middle Cerebral Artery (MCA) who presented to LA MEKERRA imaging center. Good results can be achieved especially for midline estimation comparing with manual detection. The performance of our method is quite satisfactory with AUC of 0.845 on ROC analysis for ASPECTS score. Our approach has the potential to be used as second opinion in stroke diagnosis.
Keywords: CT scan; stroke detection; midline estimation; ASPECTS score; hemispheres comparison.
Application of Data mining techniques for early detection of Heart Diseases using Framingham Heart Study Dataset
by Nancy Masih, Sachin Ahuja
Abstract: Health care organizations accumulate large amount of healthcare data, but it is not extracted to draw hidden patterns which can prove efficient for decision making process. Data mining techniques prove useful in gaining insights by discovering hidden patterns from the data sets which remain undetected manually. Heart diseases are the main cause of mortality rate in the globe. Hence, it is critical to predict the heart diseases at early stage with more accuracy and speed to save the millions of peoples lives. This paper aims to examine and compare the accuracy of four different machine learning algorithms for predicting and diagnosing heart disease using Framingham Heart Study (FHS) data set. The output of the study confirms the most prominent features that cause heart diseases and which must be analyzed for early detection of the disease. This study will be used as prognostic information in treatment of Heart Diseases.
Keywords: Heart Disease; Prediction; Framingham heart study; Decision tree; Naïve Bayes; Support Vector Machine; Artificial Neural Network.
An Enhanced Nonlinear Filter and Its Applications to Medical Image Restoration
by Boucif Beddad, Kaddour Hachemi, Jack-Gérard Postaire, Sundarapandian Vaidyanathan
Abstract: In this work, we describe an efficient developed algorithm to enhance medical images which are corrupted by the impulsive noise. However, the main objective is to remove low and high impulsive noise density using an Enhanced Nonlinear Filter (ENLF). The employed filter performs spatial information processing to identify the pixel in an image which has been affected and restores it only by the median value of the proposed 2D moving window that have the low variance value. The proposed denoising algorithm was optimized and implemented on a fixed-point TMS320C6416 Digital Signal Processor of Texas Instruments and it was successfully tested with multiple medical images and provides very good restoration and also it gives better Peak Signal-to-Noise Ratio (PSNR) and Mean Square Error (MSE) results than the output of the well-known existed nonlinear filters. The execution time of this algorithm is also appreciable.
Keywords: Code Composer Studio; Impulse Noise; Medical Images; Nonlinear Filter; Peak Signal-to-Noise Ratio; TMS320C6416 DSK.
Design of Band-Pass Filters by Experimental and Simulation Methods at the range of 100-125 keV of X-ray in Fluoroscopy
by Goli Khaleghi, Jamshid Soltani-Nabipour, Abdollah Khorshidi, Fariba Taheri
Abstract: The determination of filters that remove low-energy and attenuate high-energy spectra does not essentially influence image quality, so it can reduce the absorbed patient dose. This work examines the impacts of thickness and filter material on contrast, resolution, absorbed patient dose and image quality. Based on the attenuation curves of elements and taking into account the cost-effectiveness and availability factors, four elements including Tin, Tungsten, Lead and Copper in different thicknesses was studied at the range of 100-125 keV. The simulations were executed using MCNPX code with an error less than 1% that represented the accuracy. Experimental data were obtained based on the results of calculations and simulations using fluoroscopy equipment. The results showed that applying the filters caused improving the resolution and image quality, and also remarkable reduction in output dose rate. In conclusion, the 0.1 mm thick lead element was taken as the most appropriate element in filtration.
Keywords: Fluoroscopy; Alpha phantom; Band-Pass filter; Lead; Tin; Tungsten; Copper; Filter thickness; Absorption edge; Image quality; Resolution; Dose rate; Attenuation curve; Output intensity ratio; Monte Carlo N-Particle - MCNP code.
The Adjuvant role of Acupuncture to treat the diabetes mellitus and its analysis using thermogram
by Raja Gomathi, S. JEYADEVI, K. HEMA LATHA, P.K. RAMALINGAM, S.P. RAJA
Abstract: This work describes the effects of acupuncture in glycemic control and validates the results using Infrared thermography. Two groups of patients undergoing diabetes mellitus treatment, are considered for experimentation. Group A is treated with both drugs and acupuncture, while group B is treated with drugs alone. The patients blood sugar and surface temperature of the foot are studied. Infrared thermography is used to take thermogram, before and after acupuncture treatment, and the effect of Acupuncture treatment is analyzed. The liver and spleen acupoints are stimulated and the temperature changes in these points are analyzed. The results show that, the foot temperature (at treating acupoints) increases after acupuncture treatment in group A and the postprandial glucose level reduces up to 20 mg/dl whereas in Group B only 6mg/dl change, is observed with negligible temperature change. The obtained results show acupuncture as an optional treatment for diabetes, with no side effects and pain.
Keywords: Acupuncture; diabetes mellitus; glycemic control; foot diagnosis; Infrared thermography;Acupoints; Postprandial Blood Glucose; Fasting Glucose; Line analysis; Spot analysis.
Large-scale brain network model and multi-band Electroencephalogram rhythm simulations
by Auhood Al-Hossenat
Abstract: Electroencephalogram (EEG) alpha oscillations play a considerable role in understanding cognitive and physiological aspects of human life, and in diagnosing neurocognitive disorders such as Alzheimers disease (AD) and dementia. In this work, we developed a large-scale brain network model (LSBNM) to simulate multi-alpha band EEG rhythms. This model includes six cortical areas in the left hemisphere and each area is implemented as a local Jansen and Rit (JR) network. The proposed model is developed using the biologically realistic, large-scale connectivity connectome. The implementation and simulations were performed on the neuroinformatics platform, The Virtual Brain (TVB v1.5.4). Experimental results show that the proposed brain network model enables the generation of the multi-alpha band of EEG rhythms at different ranges of frequencies 7-8Hz, 8-9Hz and 10-11Hz by combining the local dynamics of the JR model with connectome. This model can help physicians to understand the general mechanism of EEG rhythms, it is also helpful in accurately diagnosing neurocognitive disorders.
Keywords: Large-scale brain network model; local neural masses modelling; human connectome; The Virtual Brain package.
A Biomechanical Analysis of Prosthesis Disc in Lumbar Spinal Segment using Three-Dimensional Finite Element Modeling
by Mai S. Mabrouk, Samir Y. Marzouk, Heba Afifi
Abstract: Lumbar total disc replacement (LTDR) could be an operation for handling of chronic disc illness and spinal mutilation to safeguard a range of motion (ROM). The SB Charit
Keywords: Lumbar total disc replacement (LTDR); biomechanical model; finite element method (FEE); SB Charité™ disc; von Mises stress.
Epilepsy Detection from Electroencephalogram Signal Using Singular Value Decomposition and Extreme Learning Machine Classifier
by Nalini Singh, Satchidananda Dehuri
Abstract: Automatic detection of seizure plays an important role for both long term monitoring and diagnosis of epilepsy. In this work, the proposed singular value decomposition-extreme learning machine (SVD-ELM) classifier technique provide good generalized performance with a remarkable fast learning speed in comparison to existing conventional techniques. Here, both feature extraction and classification of EEG signal has been done for detection of epileptic seizure of human brain, taking Bonn University dataset. Proposed method is based upon the multi-scale eigen space analysis of the matrices generated using discrete wavelet transform (DWT) of EEG signal by SVD at substantial scale and are classified using extracted singular value features and extreme learning machine (ELM) with dissimilar activation functions. The proposed SVD-ELM technique has been applied for the first time on EEG signal for epilepsy detection using five class classification which produces overall accuracy of 95% (p < 0.001) with sine and radbas activation function.
Keywords: EEG; Epilepsy; DWT; SVD; ELM; Eigen value; EEG Classification; Neurons; Activation functions.
A hybrid approach for analysis of brain lateralization in autistic children using graph theory techniques and deep belief networks
by Vidhusha Srinivasan, Udayakumar N, Hualou Liang, Kavitha Anandan
Abstract: Cerebral lateralization refers to the quality of inclination and a neural function specialized towards one hemisphere of the brain over the other for a specific activity. Autism spectrum disorder (ASD), encompasses wide range of presentations including reduced language processing capacity and impaired communication. This work analyses the lateralization patterns present at the language regions of the brain for typical controls (TC), low functioning (LFA) and high functioning autistic (HFA) individuals using resting state fMRI (rsfMRI). A total of 101 participants were considered for this study. The active and inactive regions in the left and right hemisphere, responsible for language processing have been analyzed through graph theory techniques. Results showed overall left hemisphere (LH) activation for TCs while impaired LH activation for LFA and unique right hemisphere (RH) activation for the HFA group. Using Deep belief networks (DBN), the average classification accuracy of the left/right lateralization exhibited by each participant was measured. The accuracy was highest in LH for controls with 97.88% and LFA measuring 78.17% in LH while, the HFA group showed dominance at RH with 94.23%. These results were validated by a senior expert professional. Thus, this work shows the variations of hemispherical lateralization using graph theory techniques and deep learning classifier to bring out the functional differences among the ASD children who exhibit overlapping brain behavioral characteristics.
Keywords: Autism; ASD; fMRI; Lateralization; Language processing in autism; High functioning autism; Graph theory; Deep belief networks.
Characterising Leg-Dominance in Healthy Netballers Using 3-D Kinematics-Electromyography Features Integration and Machine Learning Techniques
by Umar Yahya, S.M.N. Arosha Senanayake, Abdul Ghani Naim
Abstract: The present study utilised machine learning techniques to characterise differences between dominant (DL) and non-dominant (nDL) legs of healthy female netballers during single-leg lateral jump. Electromyography (EMG) activity of eight lower-extremity muscles and 3-dimensional motion of the ankle, knee, and hip joints were recorded for both jumping (JL) and landing (LL) legs. Integrated EMG of each muscle and joints range-of-motion (ROM) in all three planes were computed. Using hierarchical clustering, two subgroups were identified in both feature subsets JL and LL. LLs subgroups exhibited significant differences (p<0.05) in ROM of all joints in at-least one plane. Support vector machine classifier outperformed artificial neural networks at recognising DL and nDL patterns in subsets LL and JL with accuracy (F-Measure) of 86.21% and 81.36% respectively. These findings suggest DL-nDL differences are more manifested during landing than during jumping, a vital coaches insight as both legs are alternatingly used during single-leg jump-landing tasks.
Keywords: Leg Dominance; Netball; Machine Learning; Surface EMG; 3D-Kinematics; Single-Leg Jump; Dominant Leg; non-Dominant Leg; Lower Extremity; Functional Asymmetry; Support Vector Machine; Artificial Neural Network; Hierarchical Clustering; Principal Component Analysis.
Monitoring optical responses and physiological status of human skin in vivo with diffuse reflectance difference spectroscopy
by Jung Huang, Jyun-Ying Chen
Abstract: Fourier-transform visible-near infrared spectroscopy was applied to analyse diffuse reflectance from human skin perturbed with three skin-agitating methods. Principal component analysis (PCA) was applied to deduce three characteristic spectral responses of human skin. Based on Monte Carlo multilayer simulation, the responses can be attributed to changes in light scattering and haemoglobin and melanin content. The eigenspectra form a basis for resolving the optical responses of human skin from diffuse reflectance difference spectra measured at different time points after the skin tissue is mechanically stressed. We demonstrate that by applying this analysis scheme on in vivo measured diffuse reflectance difference spectra, valuable information about the responses of skin tissue can be deduced and thereby the physiological status of skin can be monitored.
Keywords: diffuse reflectance spectroscopy; skin tissue; optical response; monte-carlo simulation; principal component analysis.
Neonatal Heart Disease Screening Using An Ensemble of Decision Trees
by Amir M. Amiri, Giuliano Armano, Seyedhossein Ghasemi
Abstract: This paper is concerned with the occurrence of a heart disease specifically for the
neonate, as those seriously affected may face an increased risk of death. In this
paper, a novel computer-based tool is proposed for a medical center diagnosis
aimed at monitoring neonates who are potential vulnerable to heart disease. In
particular, cardiac cycles of phonocardiograms (PCGs) are first preprocessed
and then used to train an ensemble of decision trees (DTs). The classifier
model consists of 12 trees, with bagging and hold-out methods used for training
and testing. Several feature encoding methods have been experimented with to
generate the feature space over which the classifier has been tested, including
Shannon Energy and Wigner Bispectrum. On average 93.91% classification
accuracy, 96.15% sensitivity and 91.67% specificity have been obtained from
the given data, which has been validated with a balanced dataset of 110 PCG
signals taken from healthy and unhealthy medical cases.
Keywords: Neonate; Heart Diseases; Phonocardiogram; Ensemble of Decision Trees.
False positives reduction in pulmonary nodule detection using a connected component analysis based approach
by Satya Prakash Sahu, Narendra D. Londhe, Shrish Verma, Priyanka Agrawal, Sumit K. Banchhor
Abstract: In this paper, we have proposed a connected component analysis (CCA) based approach for reducing the false positives rate (FPR) per scan in the early detection of pulmonary lung nodules using computed tomography (CT) images. The lung CT scans were obtained from Lung Image Database Consortium - Image Database Resource Initiative database. Proposed study consists of four stages: (i) segmentation of lung parenchyma through K-means clustering algorithm, (ii) nodule extraction using an automated threshold-based approach (Santos), (iii) noise removal using CCA-based approach, and (iv) detection of lung nodule by using the sphericity (roundness) feature. The results were validated against the annotated ground truth provided by four expert radiologists. The study showed a reduced FPs/scan rate of 0.76 with an overall accuracy of 84.03%. The proposed well-balanced system showed a reduction in the FPR while maintaining high accuracy in lung nodule detection and thus can be usable in clinical settings.
Keywords: K-means; multi-thresholding; connected component analysis; sensitivity; false positives.
Deep 3D multi-scale dual path network for automatic lung nodule classification
by Shengsheng Wang, Xiaowei Kuang, Yungang Zhu
Abstract: Lung cancer is the cancer with the highest mortality rate in the US. Computed tomography (CT) scans for early diagnosis of pulmonary nodules can detect lung cancer in time. To overcome the limitations of the segmentation and handcrafted features required by traditional methods, we take deep neural network to diagnose lung cancer. In this work, we propose a deep end-to-end 3D multi-scale network based on dual path architecture (3D MS-DPN) for lung nodule classification. The 3D MS-DPN model incorporates the dual path architecture to reduce the complexity and improve the accuracy of the model fully considering the 3D nature of CT scan while performing 3D convolution. Meanwhile, the multi-scale feature fusion is used to eliminate the effects which the size of lung nodules varied widely and nodules occupying few regions and slices in CT scans. Our model achieves competitive performance on the LIDC-IDRI dataset compared to the recent related works.
Keywords: Lung nodule classification; Deep neural network; Computed tomography scans; LIDC-IDRI.
New Methodology Based on Images Processing for the Diabetic Retinopathy Disease Classification
by BENSMAIL Ilham, MESSADI Mahammed, Feroui Amel, Lazzouni Mohammed Elamine, Bessaid Abdelhafid
Abstract: Diabetes is a chronic disease that cannot be cured, but can be treated and controlled. It is caused by a lack of use of insulin. In the long run, a high blood sugar level causes complications, especially in the eyes, which leads to the development of diabetic retinopathy (DR), which could be considered a serious illness if it is not diagnosed and treated as soon as it appears. Poor care could cause blindness to the sick person. In this paper, we propose a new system for early detection of the DR. The tested algorithm includes several important phases, especially, the detection of the retinal lesions caused by the disease (Microaneurysms and Hemorrhages), through pretreatment and segmentation processes, as well as the classification of the different stages of non-proliferative DR. Several classifiers have been tested and the Support Vector Machine (SVM) has given a very good sensitivity, specificity, and accuracyof 97.56%, 99.01%, 97.52%, respectively. These values show that our approach can be used for diagnostic assistance in ophthalmology.
Keywords: Diabetic Retinopathy; Extraction of microaneurysms; Detection of hemorrhages; classification of the diabetic retinopathy stages.
Brain Tumor Segmentation from Magnetic Resonance Images using Improved FCM and Active Contour Model
by Nagaraja Perumal, Kalaiselvi Thiruvenkadam
Abstract: The proposed method is based on multimodal brain tumor segmentation method (MBTSM) using improved fuzzy c-means (IFCM) and active contour model (ACM). This proposed MBTSM is present a brain tissue and tumor segmentation method that segments magnetic resonance imaging (MRI) of human head scans into gray matter (GM), white matter (WM), cerebrospinal fluid (CSF), edema, core tumor and compete tumor. The proposed method consists of three stages, Stage-1 is an IFCM method, modifying the conventional FCM for brain tissue segmentation process and this method gives comparable results than existing segmentation techniques. In Stage-2, is an abnormal detection process that helps to check the results of IFCM method by fuzzy symmetric measure (FSM). In Stage-3 is segment the tumor region from multimodal MRI head scans by modified Chan-Vese model (MCV) model. The accuracy analysis of proposed MBTSM used the parameters are dice coefficient (DC), positive predictive value (PPV), sensitivity, kappa coefficient (KC) and processing time. The mean DC values are 83% for GM, 86% for WM, 13% for CSF and 75% for complete tumor.
Keywords: Active Contour; Brain Tumor; Clustering; Magnetic Resonance Image; Segmentation.
Automated methodology for breast segmentation and mammographic density classification using co-occurrence and statistical and SURF descriptors
by Roberto Pavusa Junior, Joao C. L. Fernandes, Alessandro P. Da Silva, Marcia A. S. Bissaco, Silvia R. M. S. Boschi, Terigi A. Scardovelli, Silvia C. Martini
Abstract: This paper presents a fully automated process of segmentation and classification of mammographic images at medio-lateral oblique projections. For this purpose, we developed a new set of descriptors for determination of breast density based in the standard used in the MIAS database. The process is started with the application of new techniques in the preprocessing of the image, composed by detecting the laterality of the image, and removing the image background and its artifacts, and the identification and segmentation of the pectoral muscle. From the segments, namely breast and pectoral muscle, were extracted descriptors from histogram, co-occurrence, and points of interest analysis. The descriptors were reduced by three different techniques, Spearman correlation analysis, principal component analysis and linear discriminant analysis. The image classification is performed by two different classifiers, k nearest neighbors (KNN) and support vector machine (SVM). With the SVM classifier was achieved precision of 72.05% and with the KNN classifier was achieved precision of 91.30%. Compared to other related works, the developed pre-processing technique is promising, as well as the descriptors used for density classification, which surpassed most of previous works that used all images from the database.
Keywords: Breast density; mammography; computer-aided diagnosis; SVM; KNN; SURF.
An effective Fast Conventional pattern measure based suffix feature selection to search gene expression data
by Surendar A
Abstract: Biomedical gene sequences are incompletely or erroneously annotated because of a lack of experimental evidence or prior functional knowledge in sequence datasets. Identifying the genomic useful selections instead of relying on correlations across large experimental datasets or sequence similarity remains a problem. This study proposes a Fast Conventional suffix feature pattern search algorithm(FcsFPs) for searching the gene sequence from expression data using fast feature pattern by measuring the conventionality of search accuracy from gene expression dataset. The aim is to obtain an efficient search algorithm. In this case, features from state matrix and sequence centers are described in the form of a string and the assignment of points to different sequences is done by suffix term search. Overall, the conventional pattern selection reduces computing complexity of fast gene search, improves the accuracy of searching accuracy, and reduces time complexity and the dimensionality of nonlinear gene expression data.
Keywords: gene search; pattern matching; suffix point; sequence data; throughput; gene expression; genome sequence; feature selection; clustering; suffix feature.
An effective morphological-stabled denoising method for ECG signals using wavelet based techniques
by Hui Yang, Zhiqiang Wei
Abstract: Wavelet transform has been identified as an effective denoising method for ECG signals with its advantage of multi-resolution analysis. However, it should be noted that import morphological features, such as peak of the QRS complex, should be retained after denoising for further medical practice. In this paper, an effective morphological-stabled denoising method for ECG signals is proposed though optimal selection of wavelet basis function, designing a new threshold method, optimizing decomposition levels and thresholding scheme. When validated in the MIT-BIH Arrhythmia Database, the denoising method achieved Mean Square Error and Signal-to-Noise value of 0.0146 and 68.6925 respectively, while successfully retained the QRS complex amplitude close to its full amplitude. Also, a total of 23 simulations were carried out to compare our proposed method with other methods. The experimental results indicate that the proposed denoising method can outperform other state-of-the-art wavelet-based methods while remain stable in morphology.
Keywords: ECG denoising; noise; morphology; QRS complex; wavelet transform; basis function; multi-resolution; thresholding.
Segmentation of Liver Computed Tomography Images using Dictionary based Snakes
by SHANILA NAZEERA, Vinod Kumar R S, Ramya Ravi R
Abstract: In medical research, segmentation can be used in separating different tissues from each other, through extracting and classifying the features. Segmentation of liver from computed tomography (CT) and magnetic resonance imaging (MRI) is a challenging task. Many image segmentation methods have been used in medical applications. In addition to the briefing of the need, concept and advantages of a few liver segmentation methods, this paper introduces a novel approach for the segmentation of liver computed tomography images using dictionary snakes. The performance of the proposed method is quite satisfactory.
Keywords: Image Processing; Liver Segmentation; Computed Tomography; Preprocessing; Active contour; Snakes; Dictionary Snakes; Segmentation.
Non-Invasive Estimation of Random Blood Glucose from Smartphone-based PPG
by UTTAM KUMAR ROY, Shivashis Ganguly, Arijit Ukil
Abstract: Traditional blood glucose meters are invasive in nature; blood is collected by needle pricking, which is painful, has a high risk of infections and damages tissues over repeated usage. Although, a few non-invasive methods have been proposed, they require very high-end costly non-portable custom devices and lack accuracy. This work presents a non-invasive estimate of the blood glucose using only smartphone based on PhotoPlethysmoGraph (PPG). The method supports 27x7 monitoring without any extra hardware. The system leverages the fact that glucose molecules enter the Red Blood Cells (RBC), attach to hemoglobin and affect blood color. We cleaned the noise PPG signal and extracted the red component from PPG of 25 patients, applied non-linear regression to estimate glucose and cross-validate against laboratory invasive method. The RMS error comes out to be 2.1525 mg/dL which is superior to existing non-invasive techniques. Three methods viz. geometric regression, Bland-Altman analyses and Surveillance Error Grid are used to prove the correctness.
Keywords: Non-invasive measurement; Blood glucose estimate; Regression; PhotoPlethysmoGraphy.
Non-rigid Registration (Computed Tomography Ultrasound) of Liver Using B-Splines and Free Form Deformation
by Romel Bhattacharjee, Ashish Verma, Neeraj Sharma, Shiru Sharma
Abstract: Medical Image registration is a key enabling technology and a highly challenging task. Medical images captured using different modalities (sometimes same modality) undergo the process of registration for applications like the diagnosis of a tumor, image-guided surgery, image-guided radiotherapy, etc. By iteratively minimizing a cost function and optimizing transformation parameters, the registration is achieved. In this paper, the semi-automatic non-rigid registration method is utilized in order to register computed tomography (CT) and ultrasound (US) images of the liver. The global motion is modeled by an affine transformation, while the local motion is described by Free Form Deformation (FFD) based on B-Splines. As the existence of local deformation between US and CT images is inevitable due to respiratory phases, two different techniques are included and investigated for registration refinement: transformation using Multi-level B-splines and using gradient orientation information. This work also includes and inspects three different types of optimization strategies: Steepest Gradient Descent, quasi-Newton and Levenberg-Marquardt method. This method is tested on six clinical datasets, and quantitative measures are assessed. Visual examinations and experimental results verify a lower level of registration error and a higher degree of accuracy when the method is employed using Levenberg-Marquardt optimization while utilizing the gradient orientation information for registration refinement.
Keywords: non-rigid registration; free form deformation; multilevel B-Splines; gradient orientation information.
EVALUATION OF THE MECHANICAL BEHAVIOR OF A BIPOLAR HIP PROSTHESIS UNDER TRANSIENT LOADING
by Rabiteja Patra, Harish Chandra Das, Shreeshan Jena
Abstract: Most of the studies available in the open literature make use of static analysis and discretization of the load components for studying the mechanical behavior of implants and prosthesis. The present study discusses the effect of time-varying loading on the prosthesis and femur bone assembly. The solid model of the femur bone was reconstructed using femur bone slices obtained from computed tomography (CT). The components of the hip joint forces and moments were applied at the femoral head of the prosthesis. The results from the present study were compared with the data from literature, and the present study shows that a time-varying loading analysis can provide much more realistic information about the prosthesis as compared to the prevailing use of static analyses.
Keywords: transient loading; CT; gait; finite element analysis; femoral prosthesis.
COLOR SPACE BASED THRESHOLDING FOR SEGMENTATION OF SKIN LESION IMAGES
by Sudhriti Sengupta, Neetu Mittal, Megha Modi
Abstract: In Computer Aided Diagnosis (CAD) of various skin diseases, the skin lesion image segmentation is an important phase. The quality of skin lesion images is severely affected by various factors such as poor contrast, low illumination, complexity of texture and presence of artifacts like hair etc. Thus, the existing image segmentation techniques used in diagnosis of various skin lesions are not appropriate. For better skin lesion detection, these limitations are overcome by an improved color space-based split-and-merge process in combination with global thresholding segmentation and color space technique. The obtained results have been further enhanced by self-guided edge smoothing-color space technique. The effectiveness of the proposed self-guided edge smoothing-color space technique has been verified by quantitatively comparing the obtained results with the existing Otsu thresholding, adaptive thresholding and color-space techniques. The computed results show much better values of performance measuring parameters viz.-entropy, dice similarity index and Structural Content for edge smoothing-color space technique. This indicates far superior quality of images obtained by the proposed self-guided edge smoothing-color space technique in comparison with existing Otsu, adaptive and color space techniques. The proposed technique may assist the medical professionals in early and accurate detection of skin lesions and associated diseases for benefit of patients.
Keywords: Skin lesions; Segmentation; Color space; Thresholding;Entropy; Merging; Split;Adaptive Thresholding; Otsu Thresholding; Global Thresholding;Skin diseases; Self-guided Edge Smoothing.
Dual Feature Set Enabled with Optimized Deep Belief Network for Diagnosing Diabetic Retinopathy
by Shafiulla Basha, K. Venkata Ramanaiah
Abstract: In DR detection, there are a lot of challenges to be faced in order to provide better performance and accuracy. The problem that still remains in DR detection is selection of image features, and classifiers for appropriate datasets. In order to develop a better detection method, this paper intends to propose an advanced model for detecting DR using fundus images. This detection model accomplishes in four phases include Preprocessing, Blood Vessel Segmentation, Feature Extraction and Classification. Initially, Contrast Limited AHE (CLAHE) and median filtering methods are used for preprocessing. For blood vessel segmentation, Fuzzy C-Mean (FCM) thresholding works well for making rough clustering of pixels. Further, the local features and morphological transformation-based features are extracted from the segmented blood vessels. Moreover, the deep learning classifier called Deep Belief network (DBN) classifies the extracted features, which detects whether the image is healthy or affected. As a novelty, the number of hidden neurons in DBN is optimized using modified Monarch Butterfly Optimization (MBO) termed as Distance-based MBO (D-MBO). To the next of the simulation, the performance of the proposed D-MBO-DBN-based DR detection model is compared over the existing models by analyzing the most relevant positive, and negative performance measures, and substantiates the overall performance.rnrn
Keywords: Diabetic Retinopathy Detection; Fuzzy C-Mean; Deep Belief Network; Monarch Butterfly Optimization; Hidden Neuron Optimization.
Machine Learning Approach for Automatic Brain Tumor Detection using Patch based Feature Extraction and Classification
by T. Kalaiselvi, P. Kumarashankar, Sriramakrishnan Pathmanaban
Abstract: Manual selection of tumorous slices from MRI volume is a time expensive process. In the proposed work, we have developed an automatic method for tumorous slice classification from MRI head volume. The proposed method is named as patch based classification (PBC). PBC uses 8
Keywords: Tumor detection; Feature extraction; Feature blocks; Brain tumor; BraTS dataset;.
3D Printing for Aneurysms Clipping Elective Surgery
by Stefano Guarino, Enrico Marchese, Gennaro Salvatore Ponticelli, Alba Scerrati, Vincenzo Tagliaferri, Federica Trovalusci
Abstract: This paper deals with the realization of 3D printed cerebral aneurysms by using the Direct Light Processing (DLP) technique. The aim was to improve the anatomy knowledge, training and surgical planning on individualized patient-specific basis. Computed Tomography Angiography and Digital Subtraction Angiography of three patients were used to create 3D virtual models by using a commercial image-processing software. The DLP technique was aimed at realizing the corresponding 3D physical models. These were firstly evaluated by the surgeons and then, if acceptable, used for the patient-specific treatment planning. All three models provided a comprehensive 3D representation of the related anatomical structure of the aneurysms improving the understanding of the surrounding vessels and their relationships. Moreover, the use of the DLP technology allowed fabricating the 3D models of the cerebral aneurysms in a low-time and low-cost consuming way.
Keywords: 3D Printing; Aneurysms; DLP; Neurosurgery; Rapid Prototyping; Solid Modelling.
Modified U-Net for Fully Automatic Liver Segmentation from Abdominal CT-Image
by Gajendra Kumar Mourya, Sudip Paul, Akash Handique, Ujjwal Baid, Prasad Dutande, S.N. Talbar
Abstract: Liver volume estimation using segmentation is the first step for liver diagnosis and its therapeutic planning. Liver segmentation from abdominal CT image has always been a universal challenge for researchers because of low contrast among surrounding organs. An automatic liver segmentation technique is extremely desired in clinical practice. In this paper, we have modified conventional U-Net architecture for automatic liver segmentation. This method will precisely delineate the boundaries between the liver and other abdominal organs and outperforms over another state of the art methods. We extensively evaluated our method on 'CHAOS challenge-2019 dataset of 20 subjects volumetric CT images. Quantitative evaluation of the proposed method is done in terms of various evaluation parameters with respect to their ground truth. Result achieved Average Dice Similarity Coefficient 0.97
Keywords: Computed tomography; liver segmentation; U-Net; semantic segmentation; Deep Learning.
Age-Related Macular Degeneration identification based on HRC layers analyses in OCT images
by Amel BEN KHELFALLAH, MESSADI Mahammed, BESSAID Abdelhafid, LAZZOUNI Mohammed Amine
Abstract: Age-related Macular Degeneration (AMD) is a very dangerous disease which usually affects the eyes of people with age above 50 years. AMD is characterized by extracellular deposition that accumulate between the retinal pigment epithelium (RPE) and the inner collagenous layer of Bruchs membrane, causing the death of RPE cells and subsequent loss of photoreceptor cells. Optical coherence tomography (OCT) imaging technique is the powerful tool that can detect at early stage the different macular abnormalities, in view of its high-resolution cross-sectional images. The purpose of this work is to separate the healthy images from AMD OCT images by analysing and quantifying the extracted HRC (Hyper Reflective Complex) layer using the image processing technique. The extracted layer is divided in to 10 quadrants. In each sample, the no. of white pixels is counted and the mean value of these pixels is then calculated. For both the Healthy and the AMD affected images, the average mean value is calculated. Based on this value, a decision rule is fixed to classify the images of interest. The proposed method showed an accuracy of 87,5%.
Keywords: Age-related Macular Degeneration (AMD); Hyper Reflective Complex (HRC); automatic segmentation; Optical Coherence Tomography (OCT); AMD classification.
Resampling schemes within a particle filter framework for brain source localization
by Santhosh Veeramalla
Abstract: One of the critical aspects of neuroscience research is locating neural sources from EEG data. The particle filter was used to locate resources due to its superior performance in tracking and prediction. The unknown number of neural sources in the EEG data is tracked by particle filters. A few adjustments to particle filters were proposed by improving resampling techniques for EEG applications to alleviate the particle degeneracy of the particle filter. Various methods of resampling should be studied and examined for localizing the neural source, evaluating its viability under the large sets of data. In this paper, we proposed a new approach for localization of the neural source of the real EEG data based on residual and residual systematic resampling methods in the particle filters. The robustness and the performance are validated by the root mean square error (RMSE), relative accuracy (RA) and the execution time. We show that with the proposed residual systematic resampling algorithm the proposed filter improves the root mean square error estimation performance, improves the exact position of the source and reduces time to run. The suggested approach for the source localization using a residual systematic resampling approach, by taking into account the efficiency measures, provides better performance than the other methods of resampling used in particle filter for source localization.
Keywords: particle filter; resampling; EEG; state estimation; source localization; inverse problem.
An IoT Based Smart Hearing Aid for Hearing and Speech Impaired Persons
by Solomon Nwaneri, Charles Osuagwu
Abstract: This paper presents a smart hearing aid designed to assist individuals suffering from both hearing and speech impairment. The hardware consists mainly of a digital hearing aid unit, a Bluetooth audio receiver module and a smart phone with Android applications designed on android studio using recognizer intent and Google application programming interfaces (APIs) installed and programmed. An innovative Internet of things (IoT) based interaction between the modules enabled hearing and speech impaired patients communicate effectively through the use of the hearing aid, text-to-speech converter and speech-to-text converter. The device was tested on thirty subjects from the Ear, Nose, and Throat (ENT) clinic of Lagos University Teaching Hospital Lagos, Nigeria. The results demonstrate the effectiveness of the device in assisting patients suffering from various degrees of hearing loss. Patients with various degrees of hearing loss will benefit immensely from the use of the proposed device in communicating with others.
Keywords: Android Application; Analogue-to-Digital Converters; Digital-to-Analogue Converters; Hearing loss; Internet of Things; Hearing Loss; Smart hearing aid; Smart phone; Text-to-speech.
Mitotic Cells Detection in H&E-Stained Breast Carcinoma Images
by Afiqah Abu Samah, Mohammad Faizal Ahmad Fauzi, See Y. Khor, Jenny T.H. Lee, Kean H. Teoh, Lai M. Looi
Abstract: Breast cancer is the most common cancer occurring in women, and is the second leading cause of cancer related deaths in women. Grading of breast cancer is carried out based on characteristics such as the gland formation, nuclear features, and mitotic activities, all of which need to be correctly detected first. In this paper, we proposed a system to detect mitotic cells from H&E-stained whole-slide images of breast carcinoma. The system consists three stages, namely superpixel segmentation to group similar pixels into superpixel regions, blob analysis to separate the cells from the tissues and the background, and shape analysis and classification to distinguish mitotic cells from non-mitotic cells. The proposed system, with the histogram of oriented gradients (HOG) and Fourier descriptor (FD) as features, is able to detect mitotic cells reliably, with more than 90% true positive rate, true negative rate and overall accuracy.
Keywords: breast carcinoma; mitosis detection; superpixel segmentation; digital pathology.
Mass Detection in Mammographic Images Using Improved Marker-Controlled Watershed Approach
by Pratap Vikhe, Vaishali Mandhare, Chandrakant Kadu
Abstract: Mass detection in mammogram plays vital role for early diagnosis of
breast cancer. However, screening of masses is challenging task for radiologist,
due to contrast variation, noisy mammographic images and imprecise edges. In
this paper, improved marker-controlled watershed approach presented to segment
and detects precise suspicious regions from mammograms. Morphological
operations and threshold technique has been used in proposed algorithm, to
suppress artifacts and pectoral region. Magnitude gradient computed to obtain
mass edges. Finally, internal and external marker determined and watershed
transform applied on modified gradient image, to segregate suspicious region.
Proposed approach applied on 140 mammograms from two datasets, MIAS and
DDSM. The performance of proposed approach in terms of True Positive Fraction
yields 93.7% and 94.3% respectively, at the rate of 0.72 and 0.45 average False
Positive per Image. Thus, achieved results depicts, proposed approach gives better
results for mass detection helping radiologists in diagnosis at early stage.
Keywords: Watershed Transform; Mass Detection; Marker-Controlled; Segmentation; Mammograms.
Ease Drug Delivery: Wirelessly Controlled Medication Delivery System via Android Application
by Maham Sarvat, Suhaib Masroor, Muhammad Muzammil Khan
Abstract: Medication delivery system or syringe driver system is used for administering the predefined amount of drug, into the patient, within a specific period of time through intravenous procedure i-e patient were incapable to take the drug orally. Injected medicine or fluid is absorbed in the body via blood circulation. In the last decade, numerous authors had been studied and propose various methods related to the medication delivery system, such as touch screen syringe pump, microcontroller based syringe pump, dual syringe pump, and etc. Moreover, all these medication systems have some drawbacks such as they are manual, have a crude methodology and require constant monitoring by the medical staff. In some hospitals, a shortage of medical staff, or untrained staff further increase the drawback of these kinds of systems. In this paper, a novel approach is presented to create a cost effective wireless ease drug delivery system, which can overcome the deficiencies of all the aforesaid drug delivery systems. In the proposed ease drug delivery system, it is shown that the control and operation of the drug delivery system are performed wirelessly from the nursing counter, located within the range of 30m via an android device. The device will provide information of all the installed drug delivery systems on a single screen. Moreover, it requires only a single staff member to monitor them and give them necessary instructions via the same android device. Thus, the proposed system can overcome all the shortcomings of the older drug delivery systems.
Keywords: Electro-Medical Instrument; Syringe Pump; Wireless Control.
Performance analysis of different segmentation methods applied to positron emission tomographyimages fusion
by Abdallah Mehidi, Malika Mimi, Jerome Lapuyade-Lahorgue
Abstract: Medical imaging provides objective quantitative functional information leading to decision-making on diseases. Image segmentation is of great importance in extracting this information. The labeling of regions of interest on all these volumes is an issue for automatic or semi-automatic segmentation methods. The objective of this paper is to present and analyze the main techniques of PET image segmentation and to provide a comparative study of all methods in terms of precision, accuracy assessment and reproducibility. We report the most recent results of tumor image segmentation that are used in literature. Six state-of-the-art tumor segmentation algorithms are applied to set of PET tumors which are characterized by the following properties: noise levels, wide range of contrast, uptake heterogeneity and complexity of the form by considering clinical tumor cases. The obtained results show that the Fuzzy Locally Adaptive Bayesian (FLAB) provides superior accuracy and higher precision compared to the recently used methods namely Hidden Fuzzy Markov Fields (HFMF) and Fuzzy Hidden Markov Chains (FHMC) as well as other clustering-based approaches like Fuzzy C-means (FCM), Fuzzy Local Information C-Means (FLICM) and Automated Generalized Fuzzy C-means (GFCM) with estimated norm less than 3. Furthermore, we show that the GFCM achieves the best results outperforming all other techniques when the estimated norm values, noted Norm, are greater than 3.
Keywords: Image Segmentation; Clustering Methods-Bayesian Segmentation; Fuzzy C-means Hilbertian-norm; Positron Emission Tomography (PET); Image Fusion.
An Automatic detection of Microcalcification in Mammogram Images using Neuro-Fuzzy classifier
by Neha Shahare, Dinkar Yadav
Abstract: Breast cancer is a standout amongst the most widely recognized diseases and has a high rate of mortality around the world, significantly risking the health of the females because of insufficiency in awareness about health check-up, breast screening, and insufficient medical experts. Among existing all modalities of medical scans, mammography is the most preferred modality for preliminary examination of breast cancer. In mammogram images, micro-calcifications is one of the imperative sign for breast cancer detection. An automatic technique with considering different statistical features followed by advanced fuzzy based artificial neural network for classification and detection of breast cancer is proposed. As mammogram images suffers from different noises, anisotropic diffusion filtering method is used for pre-processing of medical scan as initial step. Further, to extract the different statistical features, combine discrete wavelet transform and grey-level co-occurrence technique is used. Finally, these extracted feature vectors are then fed as input to the advanced fuzzy based artificial neural network for classification and detection of the microcalcifications present in mammogram images. For extensive experimental analysis, mini-MIAS database is considered with sensitivity, specificity and accuracy as evaluation parameters. From qualitative and quantitative results, it is evident that the proposed classification method is achieved significant improved performance as compared to existing state-of-the-art classification technique like SVM, ANN, etc.
Keywords: Microcalcifications; Mammogram; GLCM; Cellular automata; Neuro-fuzzy.
Classification of Primary and Secondary Malignant Liver Lesions using Laws Mask Analysis and PNN classifier
by Jitendra Virmani, Dilsheen Dhoat
Abstract: A common technique to identify liver cancer is through subjective analysis of ultrasound (US) images. The process of subjective analysis and classification of ultrasound images is sometimes difficult and confusing for the radiologists. Due to limited sensitivity of US images, a computer aided classification (CAC) system is developed for differential diagnosis between malignant liver lesions (MLLs). The differential diagnosis between primary malignant i.e. Hepatocellular carcinoma (HCC) and secondary malignant i.e. Metastases (MET) lesion of the liver has been carried out using three experiments based on various ROI extraction protocols i.e. (a) IROIs and NROI extraction: Multiple IROIs have been extracted within the lesion and one neighboring ROI has been extracted from the region surrounding the lesion; (b) LROI and NROI extraction: A single largest ROI has been extracted from the region within the lesion; (c) GROI extraction: A single ROI has been extracted such that the lesion is contained within the GROI i.e. this ROI includes region inside the lesion, margin and some of the surrounding area of the lesion. For the three experiments, feature extraction has been carried out using Laws Mask analysis using 1D kernels of various resolutions i.e. 3, 5, 7, 9. Probabilistic neural network (PNN) has been used extensively for the classification task. Experiment 1 which uses ratio features obtained by dividing texture features from IROIs and texture features from NROI yields a classification accuracy of 78.8 % using Laws Mask of length 7. Experiment 2 which uses ratio features obtained by dividing texture features from LROI and texture features from NROI yields a classification accuracy of 90 % using Laws Mask of length 3. Experiment 3 which uses GROI extraction yields a classification accuracy of 90 % using Laws Mask of length 7. Feature vector yielding maximum accuracy in Experiment 2 and 3 were concatenated to yield a concatenated feature vector (CFV) consisting of Laws Mask of length 3 carried out for LROI and NROI extraction and Laws Mask of length 7 carried out for GROI extraction. It has been observed that Experiment 4 yields an accuracy of 93 %.
Keywords: Focal liver lesions; Malignant liver lesions; HCC; MET; B-Mode Ultrasound images; Laws’ Mask Analysis; Probabilistic neural network classifier.
Swarm Optimization Based Bag of Visual Words Model for Content-Based X-Ray Scan Retrieval
by K. Karthik, S. Sowmya Kamath
Abstract: Classification and retrieval of medical images (MedIR) are emerging applications of computer vision for enabling intelligent medical diagnostics. Medical images are multi-dimensional and require specialized processing for the extraction of features from their manifold underlying content. Existing models often fail to consider the inherent characteristics of data and have thus often fallen short when applied to medical images.rnIn this paper, we present a MedIR approach based on the Bag of Visual Words (BoVW) model for content-based medical image retrieval. When it comes to any medical approach models, an imbalance in the dataset is one of the issues. Hence the perspective is also considering a balanced set of categories from an imbalanced dataset. The proposed work on BoVW model extracts features from each image are used to train supervised machine learning classifier for X-ray medical image classification and retrieval. During the experimental validation, the proposed model performed well with the classification accuracy of 89.73% and a good retrieval result using our filter-based approach.
Keywords: Content Based Medical Image Retrieval; Image classification; Visual Space Modeling.
Hierarchical Fusion in Feature and Decision Space for Detection of Valvular Heart Disease using PCG Signal
by M.K.M. Rahman, Ainul Anam Shahjamal Khan, Tasmeea Rahman
Abstract: Detection of valvular heart disease from phonocardiogram (PCG) signal is an important non-invasive and low-cost tool that has can have a big impact on the health care market. We have developed two techniques namely Weighted Fusion of Features in Decision Space (WFFDS) and Hierarchical Fusion in Feature and Decision Space (HFFDS) that combined information from multiple feature domains to improve the disease-detection accuracy. We have shown that fusion of multiple features improve the detection-accuracy compared with individual features. The accuracy is further improved by WFFDS technique, where the fusion is performed in decision space instead of feature space. In WFFDS, classifiers of same type are trained on different feature sets and some weights are calculated from confusion-matrix, which are then used to combine information in decision space for classifying new data. In HFFDS, fusion is performed both in feature and decision space. Our experimental results corroborate that both WFFDS and HFFDS performs better than traditional representations of features and their straight-forward fusion.
Keywords: Phonocardiogram; valvular diseases; neural network; feature fusion; decision fusion.
Detection of Abnormal Electromyograms Employing DWT Based Amplitude Envelope Analysis Using Teager Energy Operator
by Sayanjit Singha Roy, Debangshu Dey, Anwesha Karmakar, Ankita Singha Roy, Kumar Ashutosh, Niladri Ray Choudhary
Abstract: In this contribution, discrete wavelet transform based amplitude envelope analysis is proposed for automated detection and classification of healthy, myopathy and neuropathy electromyography signals. Electromyograms of healthy, myopathy and neuropathy classes were initially decomposed into several frequency bands with the help of discrete wavelet transform based multi resolution analysis. Following this, instead of using Hilbert transform, a novel technique for amplitude envelope extraction from different decomposed frequency subbands was performed using discrete energy separation algorithm implementing Teager energy operator. Three distinct features were extracted from the amplitude envelopes of each subband and analysis of variance test was carried out to measure their statistical significance. The extracted features were finally served as input to a support vector machines classifier to classify different categories of electromyography signals. It was observed that 100% classification accuracy is obtained in this work, which is found to outperform the existing methods studied on the same database.
Keywords: Classification; electromyograms; envelope analysis; support vector machines and Teager energy operator.
Early Onset/Offset Detection of Epileptic Seizure using M-band Wavelet Decomposition
by Yash Vardhan Varshney, Garima Chandel, Prashant Upadhyaya, Omar Farooq, Yusuf Uzzaman Khan
Abstract: Early detection of the seizure and its diagnosis play an important role for effective treatment of epileptic patients. Most of the research used in this field has been focused on detection of the seizure. However, it is also very important to detect seizure with minimum delay, which can be useful to take care of the patient. In this paper, an efficient approach for seizure detection with low onset/offset latency is proposed using three-band wavelet decomposition. Variance and higher order moments are computed from wavelet based feature extracted using three level wavelet decomposition. For comparative analysis, the extracted features are classified using two classifiers; decision tree (DT) and a shallow artificial neural network (ANN). The DT shows better classification performance as compare to ANN with classification specificity, sensitivity and accuracy of 99.6%, 98.97% and 99.49% respectively with onset and offset latency of 4.01s and -0.21s.
Keywords: Onset/Offset Seizure Detection; M-band Wavelet Transform; Decision tree (DT); Shallow network.
Fully automatic segmentation of LV from Echocardiography images and calculation of Ejection Fraction using Deep Learning
by Pallavi Kulkarni, Deepa Madathil
Abstract: Echocardiography is a widely used ultrasound imaging technique for cardiac health diagnosis. Echocardiography segmentation is a crucial process to evaluate multiple cardiac parameters like ejection fraction, heart wall thicknesses, etc. Recently machine learning techniques especially deep learning using convolution neural network models are finding increasing applications for echo image analysis including its segmentation. In this paper, we have presented a unique convolution neural network (CNN) model for automatic left ventricle (LV) segmentation of echo images. Denoising and feature extraction processes are integrated with the CNN model to enhance its prediction accuracies after training. The proposed system is trained on two-dimensional sequence images of 60 patients and tested on data of 22 patients. An automatic method for evaluation of ejection fraction is appended using the LV segmentation predictions generated by the CNN model. The performance of this CNN architecture is evaluated using various similarities and distance based majors as well as ejection fraction correlation with ground truth segmentation labelled images. CNN layer visualization methods are applied to obtain deeper insight into the trained network.
Keywords: Echocardiography; Left ventricle; Convolutional Neural Network; Autoencoders; feature extraction; Layer Visualization.
Optimal Wavelet based Multi-Modal Medical Image Fusion with Quantitative Analysis for Color Images using different Color Models
by Rekha R. Nair, Tripty Singh
Abstract: The component generally used to discriminate and recognize information is color and is considered as one of the most important aspects of vision. Abundant information contained in the color image can be utilized for multiple purposes such as image analysis, object identification, and extraction of powerful details. This paper proposed an Optimal Wavelet Color Image Fusion(OWCIF) algorithm for Multi-Modal medical images and can work with source images of any size. The proposed algorithm works with grayscale and color images. OWCIF composed of the Logarithmic and Wavelet domain of the transformed color model of source images. The Local Energy fusion rule provides sharp edge details. The experiment is conducted on eight color models with four different proposed algorithms. The evaluation of the OWCIF algorithm performance is demonstrated with the help of four sets of color standard data set images. The images usedrnin this work are MRA, MR-T1, CT, PET, MRI, and SPECT. Subjective evaluation ofrnfusion result is carried out by the assistance of expert Radiologists. The four proposed OWCIF algorithms compared each other to identify better algorithms and color models for the set of given images.
Keywords: Medical Image Fusion; Logarithmic Wavelet; Color Model; WhalernOptimization Algorithm.
EEG Wavelet Packet Power Spectrum Tool for Checking Alzheimers Disease Progression
by Rui Miguel Cunha, Gabriel Silva, Marco Alves, Bruno Catarino Bispo, Dílio Alves, Carolina Garrett, Pedro Miguel Rodrigues
Abstract: Nowadays Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases and it is strongly associated with age. There are four stages of AD: Mild Cognitive Impairment (MCI), Mild, Moderate (ADM) and Advanced (ADA). It has no cure, although there are treatments that can slow down the symptoms. Therefore, a correct diagnose is needed to delay the effects of the disease. This work aims at developing a new tool capable of distinguishing the different stages of AD at scalp level. Features such as the conventional frequencies relative power of the maximum, mean and minimum Power Spectral Density Wavelet Packet Transform (PSDWT) have been extracted from the Electroencephalogram signals (EEG). These features were then selected per electrode to feed four classifiers: Random forest decision trees (CT), linear and quadratic Support-Vector-Machines (SVM) and Linear Discriminant Analysis (LDA).The obtained results were analysed through topographic maps and enabled the distinguish between binary groups with the following overall accuracies: 85.5% (C-MCI); 88.2% (C-ADM); 91.4% (C-ADA); 89.7% (MCI-ADM); 82.4% (MCI-ADA) and 81.3% (ADM-ADA). It is also important to emphasise that there are zones at scalp level with different activities as the disease progresses (100% of accuracy achieved at least in one channel in binary comparisons). The applied method was able to detect major differences in scalp areas above the frontal and temporal lobes of the brain, with great accuracy (100%), as AD progresses.
Keywords: Alzheimer's disease; Mild Cognitive Impairment; Power Spectral Density; Wavelet Packet Transform; Electroencephalogram signals; Classifiers.
A wearable system to analyze tremors in the presence of external stressors
by Vanitha K M, Viswanath Talasila, Narasimha Prasad L V
Abstract: This paper focuses on the development of a low-cost wearable sensing system to detect physiological and pathological tremors. The spirogram analysis for tremor detection is performed in a novel setting. In addition, the designed wearable system is capable of analyzing tremor in other functional task contexts, apart from just handwriting analysis. Further, subjects are exposed to external stressors before they perform the spirogram task. Our results present a preliminary indication that motor control degradation, beyond a certain level of external stressors, may be limited.
Keywords: Tremor; Physiological Tremor; Pathological Tremor; Rehabilitation; Spirogram.
Assessment of Meditation Effects Using Heart Rate Variability Analysis
by Aboli Londhe, MIthilesh Atulkar
Abstract: Meditation claimed to regularize the autonomic nervous system (ANS) and causes reduced metabolic movement, inciting physical and mental relaxation. It is being looked upon as the future integrative mean of improving quality of life. The most accessible organ for assessment of ANS activities is heart and its oscillations. The heart rate variability (HRV) analysis has been emerged as a successful non-invasive method elucidate changes of sympathetic and vagal activity. The alternations of a heart are complex and constantly changing, which allows the cardiovascular system to rapidly adjust to sudden physical and psychological changes. In this paper, the exhaustive overview of HRV analysis attempts for evaluating meditation effects is presented. Moreover, The HRV metrics, their clinical significance, applications and reported usefulness in meditation assessment are presented.The variations in HRV have been analyzed using both linear and nonlinear parameters for both meditators and non-medidators.The effect of two meditation techniques namely, Chi and Kundalini Yoga meditation on HRV has been investigated extensively and significance of these techniques have been evaluated using statistical analysis.
Keywords: Meditation; Heart Rate Variability; Linear; Non-Linear; Chi; Kundalini Yoga.
Automated pathological lung volume segmentation with anterior and posterior separation in X-ray CT images
by Anita Khanna, Narendra D. Londhe, S. Gupta
Abstract: 3D volume lung segmentation is a precursor for morphometric and volumetric analysis. The proposed work is a fully automated lung segmentation method with due attention given to left and right lung separation in the anterior and posterior sections involving new concept of bounding box. The method proceeds in three steps: firstly, lung segmentation performed with morphological operations. Secondly airways extracted using 3D region growing. Finally, left and right lung lobes separated by analysing bounding box characteristics of each image. The performance matrices and net volume of lung have been evaluated with manual analysis and the results are quite satisfactory with average F1 score 0.983, precision 0.989, recall 0.976, specificity 0.998 and Jaccard index 0.965 and comparative lung volumes. The proposed method showed the consistency with reliability index of 97.72%. The time taken for complete segmentation for each subject is between 60-70 sec on Intel Core i7-8750H, CPU @ 2.20 GHz.
Keywords: computed tomography; 3D lung segmentation; region growing; airways detection; bounding box; reliability index.
Automated detection and grading of prostate cancer in Multiparametric MRI
by Prashant Kharote, Manoj Sankhe, Deepak Patkar
Abstract: The objective of this paper is to develop a transparent and meticulous feature learning framework for prostate cancer detection and grading of prostate cancer using Multiparametric Magnetic Resonance Images (mpMRI). Automated segmentation of prostate from MRI is crucial task in image guided intervention. Prostate cancer is confined by applying approved rules for prostate cancer diagnosis from mpMRI data. The clustering is performed on Apparent Diffusion Coefficient (ADC) and Diffusion Weighted Images (DWI) to obtain a probabilistic map which confirms cancerous region. The performance of presented method is enormously figured out on the dataset that contains T2-Weigted, DWI and ADC map images of 236 subjects. Total 218 regions included for analysis with 53 non-cancerous regions and 165 cancerous lesions. We obtained tumor detection accuracy of 93.2% and AUC of 0.94 by using random forest classifier. The results yield by proposed algorithm is validated by two experienced radiologists. rn
Keywords: Prostate; segmentation; deformable model; multiparametric magnetic resonance imaging (MPMRI); atlas based segmentation; active contour model; deep learning; PIRADS; prostate cancer; classifier.
Rapid Detection of COVID-19 from Chest X-Ray Images using Deep Convolutional Neural Networks
by Sweta Panigrahi, U.S.N. Raju, Debanjan Pathak, Kadambari K.V., Harika Ala
Abstract: The entire world is suffering from the corona pandemic (COVID - 19) since December 2019. Deep Convolutional Neural Networks (Deep CNN) can be used to develop a rapid detection system of COVID-19. Among all the existing literature, ResNet50 is showing better performance, but with three main limitations i.e., 1. Overfitting, 2. Computation cost, and 3. Loss of feature information. To overcome these problems authors have proposed four different modifications on ResNet50, naming it as LightWeightResNet50. An image dataset containing Chest X-ray images of coronavirus patients and normal persons is used for evaluation. 5-fold cross-validation is applied with transfer learning. Ten different performance measures (True Positive, False Negative, False Positive, True Negative, Accuracy, Recall, Specificity, Precision, F1-Score and Area Under Curve) are used for evaluation along with fold-wise performance measures comparison. The four proposed methods have an accuracy improvement of 4%, 13%, 14% and 7% respectively when compared with ResNet50.
Keywords: COVID-19 Diagnosis; Chest X-Ray images; Deep CNN; Transfer Learning; Cross-validation.
Evaluation of protein/polysaccharide blend biopolymeric material for fabrication of drug eluting wound dressing
by Shailendra Shera, R.M. Banik
Abstract: Silk fibroin protein and polysaccharide xanthan was mixed in three ratios i.e 80:20 (SFX82), 60:40 (SFX64) and 50:50 (SFX55) to fabricate blended dressing and functionalized with antibiotic amoxicillin. The dressings exhibited sustained release of incorporated antibiotics for prolonged period which helped in maintaining therapeutic concentrations of drug for quick wound recovery. The dressings showed biphasic release profile i.e. burst followed by sustained release. SFX64 showed highest cumulative drug release among all three dressing. Further, SFX64 exhibited smoother surface leading to less bacterial adhesion. Changes in wound size and histological assessments of wound tissues over time confirmed that amoxicillin loaded dressings showed faster healing, higher wound closure rate, regular and thicker formation of epidermis. SFX64 dressing was the best performer with pronounced sustained delivery of antibiotic at therapeutic concentration, smoother surface, and maximum wound recovery of 99.12
Keywords: Silk fibroin; Xanthan; Blends; Biphasic; Wound healing; Wound dressing; Sustained drug release; Bacterial adhesion; Invivo wound healing; Histology.
A Review on Wheelchair and Add-in Devices Design for Disabled
by SATEESH REDDY AVUTU, Sudip Paul, Venkateswara Reddy B
Abstract: Owing to rapidly aging populations and rising road accidents, the daily use of wheelchairs, which has become necessary to aid mobility for the disabled, is growing globally. The patients with spinal cord injuries, cerebral palsy, and those inflicted with seizures need a wheelchair. The authors expect that the information gathered within this research will enhance the understanding of modern-day wheelchair requirements. This article presents the global research campaign, starting with a debut to the wheelchair and the communities they serve. Technological inventions focus on probably the most researched regions, creating one of the most interesting for future research and development. This article reviews the role of wheelchairs for different disabilities by examining its respective merits and demerits. It highlights the gap between the associated technological features and capabilities, including the navigation and motion control methods, pros and cons of indoor-outdoor navigation on different surfaces such as standard, sandy, muddy and hilly terrain when using a wheelchair. Concerns related to the improvement of the disabled, their living conditions have concluded.
Keywords: Assistive Device; Ergonomic Design aspects; Indoor-Outdoor Navigation; Rehabilitation; Wheelchair Technologies.
Mammograms enhancement based on multifractal measures for microcalcifications detection
by Nadia Kermouni Serradj, Messadi Mahammed, Lazzouni Sihem
Abstract: The breast cancer is the most common cancer in women and represents its leading cause of death in the world . The microcalcifications (MCs) are the essential signs of precancerous cells. Their small size makes them difficult to detect and locate, hence the need of developing Computer Aided Detection (CAD) systems for early detection of breast cancer. In this paper, an approach of MCs detection is proposed. Our system includes three phases. In the first, we start by a preprocessing step to remove various noises, followed by a step of intensity enhancement based on the haze removal algorithm. The third step is based on multifractal measures to construct the ?-image which enhance MCs contrast. The proposed method was tested on three databases with a set of 371 images and evaluated in terms of PSNR and sensitivity. The obtained results are very significant and better compared to other approaches proposed in the literature.
Keywords: multifractal measure; contrast enhancement; microcalcifications; mammogram images.
A Review on Prediction of Diabetes using Machine Learning and Data mining Classification Techniques
by Abhilash Pati, Manoranjan Parhi, Binod Kumar Pattanayak
Abstract: Machine Learning (ML) and Data Mining (DM) techniques have grown in popularity among the researchers and scientists in various fields. Healthcare industry could not be an exception to it. ML and DM have become the powerful tools in prediction of various diseases. Diabetes or Diabetes Mellitus, a gaggle of metabolic disorder, can be caused due to age, obesity, lack of exercise, hereditary diabetes, living style, bad diet, hypertension, etc. and for that the entire body system can be affected harmfully and be able to capture dangerous diseases like heart disease, kidney disease, stroke, eye problem, nerve damage, etc. For this, we tried to go for a systematic review on diabetes by applying ML and DM classification algorithms for prediction and diagnosis. From the study, it may be concluded that Random Forest (RF) and Support vector machine (SVM) are to be the most successful and widely used methods for predicting diabetes. Concerning the sort of knowledge, medical datasets as well as Pima Indian Diabetes Datasets (PIDDs), provided by the UCI-ML Repository, were mainly used. This survey has been done on the applications of ML and DM classification approaches that may be useful for further investigation in predictions and resulting valuable knowledge on Diabetes.
Keywords: Diabetes Mellitus; Prediction; Machine Learning (ML); Data Miningrn(DM); Classification Techniques.
Suicidal Behaviour Screening using Machine Learning Techniques
by Anju Bhandari Gandhi, Devendra Prasad, Umesh Kumar Lilhore
Abstract: In a fast-growing world, patients of anxiety and depression are more vulnerable to attempt an obnoxious step like suicide. Therefore periodic screening of these patients can be done for their wellbeing as well as to stop the negative flow of energy. We aimed to explore the potential of Machine Learning to identify and predict Suicidal Behavior in patients with anxiety and stress by comparing the performance of Machine Learning Algorithms (Logistic Regression, Random Forest, Decision Tree, Multi-layer Perceptron Classifier). The analysis is performed using a python programming language for the screening of patients aiming to predict the risk of suicides. Random forest classifier outperforms with an accuracy of 95%. This current research work leverages the application of machine learning in the domain of the healthcare sector in the automated screening of patients. This Artificial Intelligence based solution reduces time consumption. This present kind of analysis can affect a remarkable monitoring system for healthcare departments.
Keywords: Machine Learning; Suicidal features; Jupyter; depression; counselling.
A Convex Optimization Approach to Parallel Magnetic Resonance Imaging Reconstruction
by Ifat Al Baqee
Abstract: In parallel magnetic resonance imaging (pMRI), the image reconstruction with unknown coil sensitivity functions is known as a non-convex problem in the existing literatures. The analysis of this paper shows that there exists a convex solution region in the space of the magnitude image and sensitivity encoded image functions, which contains the true magnitude image solution. The derivation of the convex solution region resolves the non-convex difficulty and leads to a convex optimization formulation of the pMRI reconstruction problem. The formulated problem consists of two steps. Each of the steps solves a regularized convex optimization problem and provides a globally optimal solution, in the sense that the solution optimizes the performance index and is independent of the initial conditions. The applications of the proposed two-step optimization to in-vivo and phantom data sets result in superior pMRI reconstruction performance compared with state-of-the-art algorithms.
Keywords: Medical imaging; Parallel magnetic resonance imaging; MRI reconstruction; Convex optimization; Regularized optimization.
Thermo Regulated Infant Warming Wrapper with infrared light emitting diodes for prevention of hypothermia in preterm low birth weight babies
by Sarath S Nair, Nagesh D S
Abstract: Preterm born babies having low birth weight are subjected to heavy loss of heat due to inadequate fat deposit under their skin. This creates a reduction in core body temperature to below physiologically tolerable levels and eventually ends up in cold stress or hypothermia. In this paper, an improved method for providing a thermo neutral environment is provided making the best use of the thermal insulating properties of the polyethylene and poly urethane foam with embedded infrared light emitting diodes. Bench top testing shows the device has an average warming time of 15 minutes and retains the temperature to more than 24 hours. The warmer is tested to provide reliable operation for more than three-month period within which the baby is expected to gain normal weight. The efficacy, safety and performance of the device is tested as per international standards and results are produced. The wrapper can improve the healthcare of the new-born at large, especially for developing countries.
Keywords: Infant warmer; hypothermia; Incubator; phototherapy; radiant warmer.
Evaluation of chondrocyte culture in novel airlift bioreactor using Computational Fluid Dynamics (CFD) tools
by Aditya Anand, Sarada Prasanna Mallick, Ishan Saurav Chandel, Bhisham Narayan Singh, Pradeep Srivastava
Abstract: This study delineates the design of a novel airlift bioreactor (ALBR) with wavy draft tube, using computational fluid dynamics (CFD) for chondrocyte culture. The advantage of using wavy walled ALBR is that it enhances mass transfer when the optimum superficial gas velocity of 0.5 m sec-1 is applied. To simulate the gas-liquid flow and investigate the effects of wavy shape in the cylindrical draft tube in the internal loop of ALBR, Eulerian model in CFD was used. The correlation was established between the geometry of the ALBR and the hydrodynamics of the broth. The result of the experiment supports the fact that enhanced mixing with controlled shear in the bioreactor leads to better growth of the cell and also, significantly improves the oxygen transfer and mass transfer of nutrients by diffusion.
Keywords: airlift bioreactor; chondrocyte; eulerian model; mass transfer; diffusion.
A Seamless Healthcare Platform for total Connectivity throughout the Patients Medical Journey
by Padmini Selvaganesan, Ajay Mahajan, Alex Russell, Anton Milo
Abstract: A smart patient healthcare interface platform is proposed that seamlessly follows the patient from the first consult, through surgery, and to recovery at home. Current state-of-the-art is very fragmented, and certain portions of the patients journey are not recorded for review by the clinicians, that if recorded could improve patient outcomes. The Seamless Healthcare Platform (SHP) is designed for integration to existing hospital electronic medical platforms. This is part of a grand vision to build connectivity between patients and clinicians such that there are no walls or boundaries while delivering quality healthcare at low-cost. A physical device was developed as a proof-of-concept, along with the software, and was validated at a hospital. It was shown that the data collected was reliable and useful in creating a two-way communication between the patient and the healthcare provider, thereby improving the overall quality of healthcare provided.
Keywords: low-cost remote monitoring; patient-clinician connectivity; seamless healthcare.
A Comparative Analysis of Fall Risk Factors in Elderly and their Automatic Assessment
by Carolin Wuerich, Christian Wiede, Anton Grabmaier
Abstract: In the geriatric population, falls are a prevalent issue and can entail severe physical and psychological consequences. Fall risk assessment can provide early information in order to adopt prevention measures. However, there are many different reasons why a person might fall ranging from muscolosceletal deficits to cognitive, mental or sensory impairments, and cardiovascular diseases.
While the majority of the approaches on fall risk assessment are based on gait analyses, other methods have shown that including considerations of other possible causes can significantly improve the prediction.
Thus, for the development of an effective fall risk assessment and to choose the appropriate interventions, the underlying causes need to be identified.
This review provides an overview of fall risk factors in the elderly population outlining the correlations between the causes, symptoms and fall risk. Moreover, the state of the art of assessment methods for the identified risk factors as well as for fall risk in general is presented.
Keywords: aging; automation; cognitive decline; elderly; fall prevention; fall risk; fall risk factors; physiological decline; risk assessment.
Isolation and characterization of copper resistant bacteria from khetri copper mines and analysis of the expression of copper-induced proteins
by Shraddha Mishra, Sanjay Kumar Verma
Abstract: The present study focuses on the isolation and characterization of copper-resistant bacteria from khetri copper mines and analysis of proteins expression under copper stress in selected isolate (KH-5) using SDS-PAGE analysis. A total of 14 different bacterial colonies (KH-1 to KH-14) were isolated on media containing 2 mM of copper and were further characterized for their biochemical properties. The cross-metal tolerance study exhibited their tolerance to other heavy metals (As, Zn, Ni, Co, and Cd) along with copper. The growth curve analysis of all the isolates showed a delay in the lag phase for KH-11, KH-12, KH-2, KH-3, KH-8, and KH-9 in comparison to other strains that indicate the more robust metal resistance mechanisms in other isolates. Based on the results of all these studies, KH-5 was selected for the study of protein expression in the presence of copper stress which showed the same protein band pattern as control (non-stressed condition) without induction of any new protein band in the stressed condition. This suggests the presence of a constitutive copper resistance mechanism in the KH-5. Thus, further studies can be done to explore the copper resistance mechanism in this isolate.
Keywords: copper; tolerance; protein expression.
New Approach for Quality Analysis of the Hearing Impaired using Combined Temporal and Spectral Processing
by Hemangi Shinde, Vibha Vyas, Vikram C. M.
Abstract: This paper proposes a novel approach of combining temporal and spectral speech enhancement methods for Hearing Impaired (HI) listeners. The temporally processed speech is combined with five different types of Maximum a-Posterior (MAP) estimators, namely, Magnitude Squared Spectrum Estimator (MSSE), MSSE using posteriori SNR uncertainty, using priori SNR uncertainty, soft masking using posterior SNR uncertainty on magnitude squared spectrum and using priori SNR uncertainty on magnitude squared spectrum. The temporal, spectral and the combined temporal spectral algorithms are evaluated in terms of quality for HI listeners using noisy speech signals at -5, 0, 5 and 10 dB SNR in a cafeteria, a station, in traffic and train noise environments. The experimental results depict that the new combined temporal spectral algorithm showed significantly better results over the individual temporal, spectral methods as well as a previous combined temporal and spectral method investigated and tested by the author earlier for HI people.
Keywords: Speech Enhancement; hearing impaired; temporal processing; spectral processing; mean opinion score.
An Insight into Phantom Sensation and the Application of Ultrasound Imaging to the Study of Gesture Motions for Transhumeral Prosthesis
by Ejay Nsugbe, Carol Phillips
Abstract: Transhumeral amputees account for the largest cohort of upper-limb amputees missing a substantial amount of their upper-limb, as per combined statistics with the UK and Italy. In this work, we utilise the human motor control theory, and Penfield homunculus as a basis for providing a review and school of thought behind phantom limb sensations, pain and associated therapy. Clinical work was also conducted on five non-amputated individuals using ultrasound imaging along the humerus while participants were instructed to produce a number of hand movements. This set of results has thus suggested that mainly compound gesture motions, which involve a degree of bulk muscular recruitment, can be detected along the humerus. It is foreseen that this set of gestures can be used to explore mobility and sensation of phantom limbs by clinical rehabilitation prosthetists.
Keywords: Upper-Limb Prosthesis; Transhumeral Amputee; Ultrasound Imaging; Phantom Sensation; Homunculus; Cybernetics; Human Motor Control; Myoelectric Prosthesis; Medical Physics; Phantom Limb.
Histopathological Image Classification using Dilated Residual Grooming Kernel Model
by Ramgopal Kashyap
Abstract: Breast cancer is one of the main reasons for death among women. Deep learning and machine learning models are contributing to the early and accurate diagnosis of Breast cancer. This research aims to contribute the medical science and technology with the novel deep learning-based model to detect the small cancer cell and the precise diagnosis of the cancer cells. The proposed model takes breast cancer Histopathological Image Classification (BreakHis) and Breast Cancer Histopathological Annotation and Diagnosis (BreCaHAD) image dataset and performs strain normalization to solve the color divergence issues. After that, data augmentation with nineteen different parameters like scaling, rotation, flip, resize, gamma value is performed to solve the overfitting issues. The proposed Dilated Residual Grooming Kernel (DRGK) model is a 19-layer model that includes proposed multiscale dilated convolution (MSDC) unit. The MSDC unit uses the dilated convolutions to extract the features very effectively, to detect small objects and thin boundary without increasing the complexity. This unit combines three small units for extractions of low-level features like edge, contour, colors, detection of small objects and to enhance the receptive field without losing the image information; it makes the computation efficient. The proposed DRGK model accelerates the process along with MSDC unit and convolution, pooling, downsampling, and dilated convolution operations. The proposed model gives better performance in terms of accuracy, average precision score, precision, sensitivity, and f1 score. Experimental results show that the proposed method outperforms many state-of-the-art ones with the accuracy of 98.50%. The total memory required by the proposed model is 32.7 M where each number takes 4 bytes, so each image takes 32.7*4MB=130.8 M.B. of memory.
Keywords: Breast Cancer; Channel attention model; Contrast limited adaptive histogram equalization; Data augmentation; Deep learning; Dilated convolution unit; Dilated residual growing kernel model; Dilated spatial convolution; Strain normalization.
Evaluation of Stress Distribution During Insertion of Tapered Dental Implant in Various Osteotomy Techniques: Three-dimensional Finite Element Study
by Bhavan Chand Yemineni, Jaideep Mahendra, Jigeesh Nasina, Little Mahendra, Lakshmi Shivasubramanian, Shareen Babu Perika
Abstract: Conventional osteotomy techniques in some cases can induce higher stress on bone during implant insertion, as a result of higher torque. The aim of the present study was to evaluate and compare the stress exerted on the underlying osseous tissues during the insertion of a tapered implant using different osteotomy techniques through a dynamic finite element analysis which has been widely applied to study biomedical problems through computer aided software. In three different types of osteotomy techniques namely conventional (B1), bone tap (B2), countersink (B3), five models and implants designed per technique were prepared, implant insertion was simulated and stress exerted by implant during each, was evaluated. Comparison of stress scores on the cortical and cancellous bone at different time points and time intervals from initiation of insertion to final placement of the implant was done. There was a highly statistically significant difference between B1 & B2 (p=0.0001) and B2 & B3 (p=0.0001) groups, whereas there was no statistically significant difference in the stress scores between B1 & B3 (p=0.3080) groups at all time points of implant placement. Overall, highly significant difference was observed between the stresses exerted in each technique. Within the limitations of our study, bone tap significantly exerted lesser stresses on the entire bone than conventional and countersink type of osteotomy procedures. Considering the stress distribution at the crestal region, countersink showed lower values in comparison with others.
Keywords: FEA; finite element analysis; ANSYS; von mises; osteotomy; bone tap; countersink; cortical bone; cancellous bone; stress distribution; implant insertion; torque; mandible; dental implant; crestal bone.
Early Diagnosis of Alzheimer Disease using EEG Signals: The Role of Pre-processing
by Vinayak Bairagi, Sachin Elgandelwar
Abstract: Electroencephalograms (EEGs) have significant ability to measure the brain activity and have huge potential for the analysis of the brain diseases like Alzheimer disease (AD). EEG is a measurement of electrical signal generated from the neurons presents in the brain. These nonstationary EEGs signals show the sign of many current diseases or even give the warning about impending diseases. Three main effects of Alzheimer disease on EEG signal have been identified like signal slowing, reduction in EEG complexity and a change in the normal state of EEG synchrony. Brain computer interface (BCI) system gives a way for the detection of the preliminary stage of the Alzheimer disease based on nonlinear EEG signals. Pre-processing of the EEG decides the efficiency of this methodology. Artifacts must be removed before analyzing the EEG signals. Henceforth in recent year, pre-processing of EEG signals has got a great deal of enthusiasm for researchers. In this paper, state of art EEG pre-processing techniques is explored. This paper indicates clear and simple understanding of selected preprocessing techniques with respect to Alzheimer disease diagnosis.
Keywords: Alzheimer Disease (AD); Electroencephalogram Signals (EEG); Independent Component Analysis (ICA); Filtering; Wavelet Transform.
A Survey on Data Mining and Machine Learning Techniques for Diagnosing Hepatitis Disease
by Tabeen Tasneem, Mir Md. Jahangir Kabir, Shuxiang Xu, Tazeen Tasneem
Abstract: With the advancement of technology in recent years,rndifferent new techniques are being used for classification andrnprediction of different complex diseases, as well as to analyzernbiomedical data in the medical field. Hepatitis is a liver diseasernthat has an adverse influence on people of any age group andrngenerally no symptoms appear. Hence, the diagnosis of hepatitisrnin the early stage becomes crucial. Use of technology can easernthe process and so researchers have proposed some classificationrntechniques for early detection of hepatitis. This paper aimsrnat summarizing the up-to-the-minute techniques used for therndiagnosis and prediction of hepatitis and in order to fulfill therngoal, numerous articles from 1996 to 2020 have been investigated.rnThis research work can be helpful to develop new techniques inrnfuture by knowing the pitfalls of the previous ones.
Keywords: Hepatitis diagnosis; Data mining; Machine learning; Classification; Disease prediction.
Numerical Analysis of Artificial Hip Joints: Effect of Geometry
by Abhishek Kumar Singh, Abhishek Mishra
Abstract: The present work deals with the comparison analysis of solid and hollow hip joint implant. A three-dimensional finite element model of hip joint implant is developed using ANSYS 18.0 for determination of contact stresses, sliding distance and deformation caused due to loading on the joint in the standing condition. The finite element contact stresses generated on the contact surfaces of hip implant model along with the sliding distance has been used in for FEM analysis. Result of analysis shows that total deformation in the joint for smaller femoral head diameter is less for the hollow femoral head than solid femoral head, but as the size of the femoral head and other components are increased, total deformation in the hollow femoral head comes out as more than that of solid femoral head.
Keywords: Artificial hip-joints; solid femoral head; hollow femoral head; FEM analysis.
An Ultrasonic sensor driven obstacle detection and localization system in 3D space for Visually Impaired Persons
by Bhupendra Singh
Abstract: There are several challenges faced by Visual Impaired persons while travelling through the outdoor environment. The white canernmost commonly used by them for obstacle detection in their route hasrnits limitation with the inability to detect obstacles above waist height.rnDue to this limitation head injury is very commonly faced by the VisuallyrnImpaired persons. In this work, we have developed eyeglasses whichrnconsist of two Ultrasonic sensors and two buzzers for obstacle detectionrnand localization. The location of the obstacle in 3D space is conveyedrnto the user with varying frequency patterns through the buzzers. The 3Drnlocation of the obstacle is conveyed in terms of laterality, elevation andrndepth information. Upon testing the system for the effectiveness in detecting the obstacle in 3D space, it is found as 70.5% laterality detectionrnrate, 70.5% elevation detection rate and 80.8% depth detection rate. Onrncomparing our results with similar results reported in the literature as arnstate of the art, our results outperform them all.
Keywords: Assistive Technology,; Electronic Travel Aids; ;Healthcare,;rnSensors; ;Visual Impairment.
Simulation of insufflation gas via an alternative Multi-functional Forceps with applications in Laparoscopic Surgeries
by Md. Abdul Raheem Junaidi, Harsha Sista, Daseswara Rao Yenduluri, Ram Chandra Murthy K
Abstract: Purpose: To simulate the gas flow in a multi-functional laparoscopic instrument using ANSYS FLUENT software. \r\nMaterial and Methods:The laparoscopic procedure used by surgeons is a minimally invasive surgery to operate upon the abdominal cavity. The Suction-Irrigation (S-I) process is used to clean and disinfect the abdominal cavity to enable safe and efficient surgical intervention. In most surgeries, the dissector forceps are repeatedly exchanged with the S-I device to operate and clean the surgery site. The improved forceps is a combination of a suction-irrigator and a dissector forceps. \r\nResults:A more comprehensive CFD flow analysis of the improved forceps, the flow of CO2, is simulated in the present work for different driving pressures. The resulting flow rate of CO2 is compared among the prospective designs and the S-I device currently used. The results are investigated with the help of contours plots. \r\nConclusion:The new surgical forceps eliminates re-insertion of dissector with suction-irrigator and is reusable, multi-functional, non-toxic, corrosion-resistant, toughened, and cost-effective. In addition, this forceps aids in reducing the time of surgery, fatigue to the surgeon, and trauma to the patient. This can also potentially benefit in single port and robotic laparoscopic surgeries.\r\n
Keywords: Computational Fluid Dynamics; Forceps; Newtonian; S-I device; Insufflator; multi-functional instrument.
Development of non-contact optical device for monitoring neonatal jaundice based on the skin color of the upper trunk using skin reflectometry
by Vignesh Kumar Kanamail, Periyasamy R, Senthil Kumar K, Suresh Chelliya D, Senguttuvan D
Abstract: Jaundice occurs in new born babies within few days of birth due to elevated bilirubin levels in the blood and also the most common causes of hospital admission of young infants. In general, skin colour changes in new born are visually assessed and total serum bilirubin (TSB) level are measured through blood sampling method for identifying the severity of jaundice. Transcutaneous Bilirubin (TcB) is often preferred as an alternative method to avoid frequent blood sampling. However, this method has a challenge in dealing with neonates in countries of the Indian subcontinent where babies have distinctive skin colour. Hence the aim of this paper was to develop a non-invasive, non-contact handheld optical device (460nm LED light source and a photodiode) to measure bilirubin concentration in neonates of Indian subcontinent based on the skin reflectance. The device was tested with mock bilirubin samples (n=8), human blood serum samples (n=8) and on neonates in Neonatal Intensive Care Unit (n=39). The results were validated with TSB value and positive correlation factor of R =0.95 to 0.99 was observed between TcB and TSB by applying first order linear regression analysis. Therefore, the proposed indigenously developed device was successfully detected the jaundice by estimating the bilirubin concentration in neonates based on skin reflectance.
Keywords: Neonatal Jaundice; Non-Invasive Bilirubin Monitoring; Optical method; Skin Reflectance; Transcutaneous Bilirubin.
Implementation of machine learning algorithms for automated human gait activity recognition using sEMG signals
by Ankit Vijayvargiya, Balan Dhanka, Vishu Gupta, Rajesh Kumar
Abstract: Recognition of various human gait activities based on the sEMG signal has an important role to control the exoskeleton or prosthesis. These robotic assistive devices are used for enhancing the physical performance of an injured or disabled person. In this paper, a comparative assessment of various computational classifiers is presented for the recognition of different gait activities from the sEMG signal. Analysis of sEMG signal is complicated because of a multiple muscle contribute to a single activity and the effect of other muscles produces noise. So, first, we have applied the discrete wavelet transform to the sEMG signal based on the Daubechies wavelet and then extracted eleven-time domain features. Thereafter, features are standardized and fed to eight different computational classifiers. The performance indices of classifiers are calculated for ten runs. The results suggest that the MLP Classifier gives the highest accuracy (97.72%) in identifying different gait activities from sEMG signals.
Keywords: Human Gait Activity Recognition; Discrete Wavelet Transform (DWT); Computational Classifier; Surface Electromyography (sEMG) Signal.
Differences in Kinematic Variables in Single Leg Stance test between young and elderly people
by David Perez Cruzado, Manuel Gonzalez Sanchez, Antonio Cuesta Vargas
Abstract: Background. Parameterising the Single Leg Stance test could be useful in clinical practice and basic research. The aim of the present study was to understand the intergroup and intragroup differences in kinematic variables among young adults and older adults in the performing of Single Leg Stance test. Methods. Two groups of participants were measured, 6 individuals over 65 years old and 6 individuals between 20-25 years old. Inertial sensors were located in the trunk and in the lumbar zone. Results. Significant differences between groups were found in the lumbar and trunk sensor in different movements (flexo/extension, inclination and rotation). Significant differences between the dominant and non-dominant leg were not found. Conclusion. There were significant differences between both groups. It is also important to highlight the excellent values of reliability of the inertial sensors.
Keywords: elderly; aging; kinematics; balance; inertial sensor.
PPG based Windkessel Model Parameter Identification via Unscented Kalman Filtering
by Akhil Walia, Amit Kaul
Abstract: Modeling of arterial system is helpful in understanding the cardiovascularrnsystem and related ailments. Among various methods, Windkessel model is one approach which plays signicant role in understanding the working principle of natural arterial system. The windkessel models describe the hydraulic properties of arterial system. In this paper, PPG based windkessel model has been suggested which utilizes PPG signal as measurement. State dynamics of proposed model has also been developed. The main contribution of this work lies on the identication of model parameters using Extended Kalman lter (EKF) and Unscented Kalman lter (UKF). Estimated parameters are compared with nominal values to validate model structures. The comparative analysisrnhas been carried out with the pre-existing method. Execution time taken to simulate the proposed model for modeling a single PPG pulse is approximately one second.
Keywords: Windkessel model; Compliance; Inertance; Unscented Kalman Filter (UKF).
A novel hybrid system for detecting epileptic seizure in neonate and adult patients
by Ahmed Adda, Hadjira Benoudnine, Mohamed Daoud, Philippe Ravier
Abstract: Epilepsy is a brain disease characterized by recurrent seizures. Electroencephalography (EEG) is a prominent tool used in clinical routine for monitoring and diagnosing seizures. Visual inspection of EEG traces is a time-consuming and laborious process. The literature survey shows that though some advanced methods suggested for automatic seizure detection perform quite well in case of adult patients, they fail in discovering neonatal seizure activity, due to the fact that neonatal seizures are less prominent than adult seizures. Therefore, this research proposes a generalized automatic system for detecting seizures in epileptic patients regardless their ages. The proposed system takes advantage of hybridation between generalized Hurst exponent (GHE) and approximate entropy (ApEn) features extracted from the amplitude envelope of EEG signals. These features are taken as input parameters of the support vector machine (SVM) classifier, which distinguishes EEG signals based on the existence or not of seizures. In order to assess the generality of the proposed technique, binary test (normal vs. seizure) was achieved on two independent datasets, including Bonn University EEG database for adults and that of neonatal EEG collected at the Royal Womens Hospital, Brisbane, Australia. In the first dataset, our system detects seizures with an accuracy of 99 %, whereas in the second dataset, the proposed system reached an accuracy of 100%. The experimental results show that the proposed method demonstrates superiority to existing systems by solving the seizure detection tasks with a single automatic system, which shows very high accuracy for both neonate and adult patients. Such a system could help neurologists in the visual analysis, diagnosis of long-term EEG recordings and considerably reduces the time required for this process.
Keywords: Electroencephalogram; epilepsy; seizure; Signal envelope; Hurst parameter; Entropy.
Fetal Brain Extraction using Mathematically Modelled Local Fetal Minima
by Durgadevi Paramasivam
Abstract: Division of the cerebrum from fetal MRI is a generally new field, with little work distributed on completely programmed preparation. Programmed mind division strategies produced for MRI of fetal brain images can\'t be straightforwardly applied to consider the creating fetal cerebrum in utero, since the fetal mind is altogether extraordinary regarding math just as tissue morphology. In this paper, the proposed segmentation techniques, to separate brain parcel from the MRI of the human embryo and in forthcomings days decided to determine the abnormality of the fetal brain at various gestational weeks. Lately, an assortment of division techniques has been proposed for the programmed depiction of the fetal and neonatal cerebrum MRI. These strategies mean to characterize areas of the premium of various granularities: mind, tissue types, or more limited constructions. Various philosophies have been applied for this division task and can be grouped into the solo, parametric, characterization, atlas combination, and deformable models. Cerebrum atlases are usually used as preparing information in the division interaction. Difficulties identifying with the picture securing, the quick mental health just as the restricted accessibility of imaging information anyway thwart this division task. This paper discusses fetal brain segmentation using mathematically modelled fetal brain minima by using a curve fitting segmentation technique. Broad tests show that the proposed approach beats the ebb and flow techniques explicitly Watershed extraction, Otsus extraction, Edge detection based extraction, and Histogram based extraction. The results dictated by applying the proposed calculation and results gained are significant
Keywords: Fetal MRI; Brain Localisation; fetal minima; automatic; curve fitting; smoothing filter; thresholding; segmentation; Structural Similarity Index (SSIM).
Depression Diagnosis Using a Hybrid Residual Neural Network
by Mahsa Ofoghi Rezaei, Somayeh Makouei, Sebelan Danishvar
Abstract: Depression is one of the most widespread psychiatric disorders. EEG signals can be utilized as a tool to diagnose depression objectively. This paper employs a hybrid method to classify healthy and depressed signals, which uses a pre-trained ResNet101 to extract features automatically. Thereby, the problem of designing and training deep networks for automatic feature extraction is solved. The hypothesis in the present study is that feature-extraction layers in ResNet101 also perform desirably in detecting depressed signals. In hybrid structures, SVM, KNN, and DT classifiers are used for final classification purposes. ResNet101-SVM, ResNet101-KNN, and ResNet101-DT structures have reached accuracy of 93.8%, 90.1%, and 82.1%, respectively. Moreover, for the ResNet101-SVM structure, which has shown the best performance among all structures, the accuracy, sensitivity, and specificity are 94.7%, 94.0%, and 95.2% after applying the 10-fold cross-validation method. The results indicate the proper performance of all structures, especially the ResNet101-SVM structure, in diagnosing depression.
Keywords: Depression; Diagnosis; Classification; EEG; Deep learning; Residual network; Hybrid model; SVM; KNN; DT.
Design of Artificial Pancreas (AP) based on HGAPSO-FOPID Control Algorithm
by Akshaya Kumar Patra, Anuja Nanda
Abstract: This manuscript presents the design of Hybrid Genetic Algorithm-Particle Swarm Optimization-Fractional Order Proportional Integral Derivative (HGAPSO-FOPID) controller to inject the optimal dose of insulin through the AP for Blood Glucose (BG) regulation in Type-I Diabetes Mellitus (TIDM) patients. In this strategy, the controller parameters are tuned based on the Hybrid Genetic Algorithm Particle Swarm Optimization (HGAPSO) technique for better control execution. The productivity of the HGAPSO-FOPID controller as to accuracy, robustness and stability is tested by use of MATLAB and SIMULINK. The procured outputs reveal the better implementation of HGAPSO-FOPID controller to regulate the BG level within the range of normo-glycaemia (70 – 120mg/dl). The justification of improved control execution of the HGAPSO-FOPID controller is revealed by the relative result examination with other prominent control techniques.
Keywords: BG level; AP; MID; HGAPSO-FOPID controller; diabetes.