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International Journal of Biomedical Engineering and Technology

International Journal of Biomedical Engineering and Technology (IJBET)

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International Journal of Biomedical Engineering and Technology (73 papers in press)

Regular Issues

  • Monitoring optical responses and physiological status of human skin in vivo with diffuse reflectance difference spectroscopy   Order a copy of this article
    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.

  • Automated methodology for breast segmentation and mammographic density classification using co-occurrence and statistical and SURF descriptors   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

    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.
    DOI: 10.1504/IJBET.2019.10040008
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.
    DOI: 10.1504/IJBET.2022.10046465
  • Resampling schemes within a particle filter framework for brain source localization   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.
    DOI: 10.1504/IJBET.2020.10036183
  • Optimal Wavelet based Multi-Modal Medical Image Fusion with Quantitative Analysis for Color Images using different Color Models   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.
    DOI: 10.1504/IJBET.2020.10042785
  • Assessment of Meditation Effects Using Heart Rate Variability Analysis   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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 [1]. 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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.
    DOI: 10.1504/IJBET.2021.10048204
  • Development of non-contact optical device for monitoring neonatal jaundice based on the skin color of the upper trunk using skin reflectometry   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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 signi cant 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 identi cation 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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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   Order a copy of this article
    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.

  • Artificial Intelligence Methods for Image Classification Applied to Biological Sounds for the Early Diagnosis of Cardiorespiratory Pathologies and COVID-19 Infection   Order a copy of this article
    by Agostino Giorgio 
    Abstract: With the spread of the COVID-19 pandemic, the scientific community took prompt action to seek adequate solutions for the prevention and treatment of the disease. However, what seems less developed at present are methods for early diagnosis of the disease which would be useful especially when it is becoming more complicated towards interstitial pneumonia which is the main cause of ICU admissions and deaths. The aim of this work is to describe methods typically used for signal and image digital processing, especially artificial intelligence (AI) algorithms, which could allow a very early diagnosis of the onset of COVID-19 infection as well as many other respiratory and cardiac pathologies. For this purpose, at least for a first screening, the use of medium-capacity smartphones may also be sufficient, without the need to resort to expensive medical equipment and diagnostic tests that require long waiting times and are always onerous.
    Keywords: COVID-19; Artificial Intelligence; Digital Signal Processing; Matlab; Digital Medical Devices; Biological sounds; Auscultation.

  • Regression transfer learning for the prediction of three-dimensional ground reaction forces and joint moments during gait   Order a copy of this article
    by Goksu Avdan, Sinan Onal, Banafsheh Rekabdar 
    Abstract: Clinical gait analysis is a useful tool for assessing a patients walking conditions. Force platforms are gait analysis tools used to collect the ground reaction forces (GRFs); however, they are expensive and time-consuming. Therefore, this study focuses on the prediction of GRFs and joint moments without using force platforms. To address this problem, we proposed to combine deep learning methods with regression transfer learning (RTL). The inputs of the proposed method are joint angles and marker trajectories from a public dataset. Principal component analysis (PCA) has been used to reduce the data dimensionality to improve the computational time and prediction accuracy. A synthetic dataset has been generated to pre-train the deep learning method for transfer learning purpose. The experimental results indicate that the proposed transfer learning method increases the target domains learning process and can successfully predict the average GRFs and joint moments with 97.44% and 96.56% accuracy, respectively.
    Keywords: Regression transfer learning; Deep learning; 1D convolution neural network (1D CNN; Gait analysis; Ground reaction forces.

  • Diagnosing Cardiovascular Diseases from Photoplethysmograph: A Review   Order a copy of this article
    by Devaki V, Jayanthi Thiruvengadam 
    Abstract: The peripheral blood volume are the variations measured at the skin surface, using a source of light and a photo detector, which is performed by a non-invasive technology called Photoplethysmograph. At recent times, much interest has been shown by countless researchers worldwide to obtain valuable information from pulse waveform besides oxygen saturation measurement, pulse rate and heart rate evaluation. Photoplethysmograph based techniques are most preferable method for the wearable devices. Each derivative of pulse waveform carry beneficial information related to health. The evolution in the wearable cardiac monitoring devices has paved a way for the individualized medical care. The application of multi-spectral photoplethysmography in various areas of cardiovascular diseases such as myocardial infarction, arterial parameters estimation, arrhythmias, Heart Rate Variability detection (HRV) discussed by researchers are presented in this article. This review gives an extensive overview on currently improved technologies that are applied in cardiovascular disease diagnosis using multi-spectral photoplethysmograph
    Keywords: Cardio-vascular diseases; Non-invasive diagnosis; Photoplethysmography; Wearable devices; optical method; multi wavelength.

    by Seema , Jasbir Singh Saini, Sanjeev Kumar 
    Abstract: This paper introduces the implementation of a master slave configuration set up for robotic surgery. Image processing has been used for establishing the same. A hardware set up has been designed which interfaces with a GUI. The two work in conjunction to achieve the controlled movement of a robotic arm. The software section is divided into two sections basically, Image Dataset Selection and Validation and the development of a suitable GUI. The image dataset selected was a set of 140 MRI brain tumour related images. The designed GUI allows the surgeon to capture the patient live view and set an image for processing and using further. Also, the hardware setup is controlled from the master/ doctors end. This selected frame goes through the process of image segmentation method that had been selected and improved.
    Keywords: Haptics; master slave configuration; image processing.

  • Reduction in wear loss of Ultra-high molecular weight polyethylene composite under Human body temperature   Order a copy of this article
    by RAVIVARDHAN N A, Jagadish T 
    Abstract: Ultra-high molecular weight polyethylene (UHMWPE), a standard material used in artificial joints, generates wear debris when used in total hip replacement. In this study UHMWPE polymer reinforced with Multi-walled Carbon Nanotubes (MWCNT) has been developed to enhance the wear resistance property of the material. The compressed composite specimens were subjected to wear test at different load under room temperature and at 40C, i.e. close to human body temperature. This is the first report on the effect of human body temperature on wear properties of composite materials. Wear properties were studied for UHMWPE and UHMWPE-MWCNT composites. The study has confirmed a reduction in wear loss of UHMWPE by reinforcement with MWCNT and a high-level reduction in wear loss of the composite materials on exposure to human body temperature. The findings of this study have made a great contribution to joint transplantation therapy by providing valuable input regarding wear-resistant implant material.
    Keywords: Biomaterials; Composites; UHMWPE; MWCNT; wear loss.

  • Three Dimensional Reconstruction Of Brain Tumors From 2D MRI Scans: Optimized Curve Fitting Process   Order a copy of this article
    by Sushitha Susan Joseph, Aju Dennisan 
    Abstract: This paper intends to introduce a 3D reconstruction model along with the solution of curve fitting problem via optimization process. This helps the model to sustain the accurate construction by estimating the boundary, corner points etc. To make the better adjustment the parameters in the curve fitting process are optimized by a new Clan Updated Grey Wolf Algorithm (CUGWA), which is the hybrid version of conventional GWO and EHO algorithms. The boundary fitting is precisely done by considering the minimization of RMSE among original and fitted boundaries. Finally, performance of the adopted method is validated over other existing schemes with respect to curve fit analysis and convergence analysis.
    Keywords: 3D Reconstruction; Brain Tumor image; Parameterization; Bezier curve; Optimization.

  • Neonatal heart disease screening using an ensemble of decision trees   Order a copy of this article
    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 centre diagnosis aimed at monitoring neonates who are potential vulnerable to heart disease. In particular, cardiac cycles of phonocardiograms (PCGs) are first pre-processed 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; ventricular septal defect; machine learning; heart murmurs; time-frequency features; decision trees.
    DOI: 10.1504/IJBET.2022.10048620
  • False positives reduction in pulmonary nodule detection using a connected component analysis based approach   Order a copy of this article
    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 the lung image database consortium – image database resource initiative database. Proposed study consists of four stages: 1) segmentation of lung parenchyma through K-means clustering algorithm; 2) nodule extraction using an automated threshold-based approach (Santos); 3) noise removal using CCA-based approach; 4) 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; CCA; sensitivity; false positives.
    DOI: 10.1504/IJBET.2022.10048621
  • Deep 3D multi-scale dual path network for automatic lung nodule classification   Order a copy of this article
    by Shengsheng Wang, Xiaowei Kuang, Yungang Zhu, Wei Zhang, Haowen Zhang 
    Abstract: Lung cancer is the cancer with the highest mortality rate in the USA. 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 scan. 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.
    DOI: 10.1504/IJBET.2022.10048622
  • New methodology based on images processing for the diabetic retinopathy disease classification   Order a copy of this article
    by Ilham Bensmail, Mahammed Messadi, Amel Feroui, Amine Lazouni, Abdelhafid Bessaid 
    Abstract: Diabetes is a chronic disease that cannot be cured, but can be treated and controlled. In the long run, a high blood sugar level causes complications, especially in the eyes, which leads to the development of diabetic retinopathy (DR). 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 haemorrhages), 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 accuracy of 97.56%, 99.01%, 97.52%, respectively. These values show that our approach can be used for diagnostic assistance in ophthalmology.
    Keywords: diabetic retinopathy; microaneurysms; haemorrhages; classification; machine learning; K-nearest neighbour; KNN; support vector machine; SVM; multilayer perceptron; MLP; radial basic function; RBF; C4.5.
    DOI: 10.1504/IJBET.2022.10048623
  • Brain tumour segmentation from magnetic resonance images using improved FCM and active contour model   Order a copy of this article
    by Nagaraja Perumal, Kalaiselvi Thiruvenkadam 
    Abstract: The proposed method is based on multimodal brain tumour segmentation method (MBTSM) using improved fuzzy c-means (IFCM) and active contour model (ACM). This proposed MBTSM presents a brain tissue and tumour segmentation method that segments magnetic resonance imaging (MRI) of human head scans into grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), oedema, core tumour and compete tumour. 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. Stage 2 is an abnormal detection process that helps to check the results of IFCM method by fuzzy symmetric measure (FSM). Stage 3 is segment the tumour region from multimodal MRI head scans by modified Chan-Vese (MCV) model. The accuracy analysis of proposed MBTSM used the parameters 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 tumour.
    Keywords: brain tumour; clustering; magnetic resonance image; segmentation; active contour.
    DOI: 10.1504/IJBET.2022.10048624