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International Journal of Image Mining (6 papers in press)
Microscopic Image Analysis for Herbal Plant Classification by Bhupendra D. Fataniya, Tanish Zaveri Abstract: An identification of herbal plants from its powder form is a challenging task. In this paper, a new method for identification and classification of herbal plants liquorice, rhubarb and dhatura using the microscopic image is proposed. This paper evaluates the effectiveness of the shape and texture-based features with a different classifier for herbal plants classification. Three shape and five texture features are computed for each object. The effectiveness of the individual shape and texture-based features set and their combinations are investigated using a support vector machine, K-nearest neighbour and ensemble classifier. The highest 94.9% classification accuracy was achieved by combining all shape features using the bagged tree ensemble classifier. While using a combination of texture-based features almost 99.8% classification accuracy is obtained using fine K-nearest neighbour and cubic-support vector machine classifier. Further, by combining shape and texture-based features classification efficiency achieved is 99.3% with quadratic-support vector machine. From the analysis of simulation results, it is found that texture-based features are more effective to classify a microscopic image of herbal plants. Keywords: shape feature; texture feature; object detection; herbal plant; microscopic image. DOI: 10.1504/IJIM.2020.10029925
Segmentation of Retina Images to Detect Abnormalities Arising from Diabetic Retinopathy by AMRITA ROY CHOWDHURY, Sreeparna Banerjee Abstract: Segmentation of retina images to isolate and detect abnormalities is an important step before the classification can be performed. In this paper, we apply three popular unsupervised segmentation algorithms, namely, K means clustering, Fuzzy C means clustering and Otsu multilevel thresholding algorithm to extract dark and bright lesions caused by Diabetic Retinopathy, in the earlier stages of its prognosis. This segmentation process also helps in removing normal structures in the retina images like optic disc and blood vessel tree. The results of the best performing segmentation algorithm can subsequently be used in classification and thereby aid the ophthalmologists in diagnosing the disease. It is found that while Otsu segmentation performs best, K-means is a close second and outperforms Fuzzy C means clustering in terms of time complexity and is therefore the best choice. Keywords: Fuzzy C means; K-means; Otsu algorithms; retina image segmentation.
A novel approach of image retrieval towards segmentation by Tamilkodi R, Roseline Nesa Kumari Abstract: At the present time, it becomes simple to stockpile vast quantity of images by means of image processing approach. The hasty right of entry to these ample group of images and retrieve related images of a specified image from this massive assortment of images presents key challenges and requires competent algorithms. In this manuscript, the authors intend a structure which is proficient to select the most appropriate features to examine newly expected images in that way improving the retrieval accuracy and competence. An enhanced algorithm is projected now. The algorithm comprises of scheming feature vectors after segmentation which will be used in likeness contrast linking query image and database images. The structure is skilled for dissimilar images in the database. The projected method has been tested on a variety of real images and its concert is found to be reasonably acceptable when compared with the concert of conventional methods of content based image retrieval. The major objective of the anticipated method is to endow with an exact outcome with lesser computational time. Keywords: Segmentation; retrieval;feature extraction.
CBIR Using Content Frequency and Color Features by Youness CHAWKI, Khalid EL ASNAOUI, Mohammed OUANAN, Brahim AKSASSE Abstract: Due to the diversity of the image content, we propose in this study a new technique for Content-based Image Retrieval (CBIR) to characterize the image. In this scenario, all images are characterized by their frequency content and their color information. Indeed, using the High Resolution Spectral Analysis methods, especially the 2-D ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) we extract from the image its content frequency and with statistical moment its color information in order to construct a new vector descriptor. The experimental results applied to the Coil_100 database show the robustness of our approach, the precision average can be reaches 90.57%. Keywords: CBIR; Frequency Content; High Resolution Spectral Analysis; 2-D ESPRIT; Color feature; Statistical Moments.
Design and implementation of an efficient rose leaf disease detection and classification using convolutional neural network by K. Swetharani, G. Vara Prasad Abstract: Roses are most planted flowers in world and are grown to make profit and regarded as symbol of love. Diseases are harmful to plants health, which in turn has adverse impact on life cycle and quality of flowers. In order to ensure quality and minimum losses to cultivate, it is essential to develop an effective prevention mechanism. This paper has introduced modelling of rose plant disease classification systems based on concept of pre-trained learning mechanism of convolutional neural network. The proposed computational classification model uses multi-level pre-processing scheme as an auxiliary tool for feature learning and accurate disease dentification. The modelling of proposed model is carried out on basis of analytical research methodology with prime objective of gaining higher performance. The study outcome shows better performance with an accuracy rate of 97.3% in disease classification. The scope of proposed work is justified based on performance analysis and comparative assessment. Keywords: rose plant; deep learning; disease classification; feature extraction. DOI: 10.1504/IJIM.2021.10037234
SliceNet-AD: slice selection-based convolution neural network model for classification of Alzheimer's disease by N. Vinutha, Santosh Pattar, P. Deepa Shenoy, K.R. Venugopal Abstract: In recent days, a rapid advancement in imaging technologies has tremendously increased the collection of images in the medical field. These emerging technologies have also led the researchers to focus on computer aided diagnosis (CAD) using efficient machine learning and deep learning techniques. In this paper, we have proposed a framework for binary and multiclass classification of Alzheimer's disease (AD) using three-dimensional structural magnetic resonance images (sMRI) and clinical scores from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The collected images are subjected to pre-processing using FMRIB Software Library. After pre-processing the three dimensional grey matter tissue, obtained as an output
from tissue segmentation comprises of many two dimensional slices. But, processing and training all the slices requires a lot of computational time. Therefore our aim is to employ convolutional neural network only on the significant slices and also to report the performance of the model. Experimental results prove that the proposed slice selection classification framework achieves better performance when fused with clinical scores.
Keywords: Alzheimer’s disease; convolutional neural network; CNN; entropy; slice selection; structural magnetic resonance imaging; sMRI; computer aided diagnosis; CAD; Alzheimer’s disease neuroimaging initiative; ADNI. DOI: 10.1504/IJIM.2021.10037275