Mussarat Yasmin

A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions

Abstract Diabetic burden around the world with a consequence of diabetic retinopathy can lead to permanent blindness in patients. Exudates detection in fundus images through an automated method is a vital task that has many applications in diabetic retinopathy screening. Realizing it important, a system being proposed in this paper automatically classifies exudates and non-exudates regions in retinal images. Presented technique is based on pre-processing for candidate lesion extraction, features extraction and classification. In pre-processing, Gabor filter is applied to the gray scale image which makes it useful for lesion enhancement. Segmentation of candidate lesion is based on mathematical morphology. A features set is selected for each candidate lesion using a combination of statistical and geometric features. Presented method is evaluated via publicly accessible datasets with the help of performance parameters such as true positive, false positive and area under curve for statistical analysis. Publicly available datasets such as e-ophtha, HRIS, MESSIDOR, DIARETDB1, VDIS, DRIVE, HRF and one local dataset are used to test the suggested system. The achieved results show an average AUC of 0.98 and accuracy as high as 98.58% which are substantially higher than the existing methods.

Intelligent Image Retrieval Techniques: A Survey

In the current era of digital communication, the use of digital images has increased for expressing, sharing andinterpreting information. While working with digital images, quite often it is necessary to search for a specific image for aparticular situation based on the visual contents of the image. This task looks easy if you are dealing with tens of imagesbut it gets more difficult when the number of images goes from tens to hundreds and thousands, and the same contentbasedsearching task becomes extremely complex when the number of images is in the millions. To deal with thesituation, some intelligent way of content-based searching is required to fulfill the searching request with right visualcontents in a reasonable amount of time. There are some really smart techniques proposed by researchers for efficientand robust content-based image retrieval. In this research, the aim is to highlight the efforts of researchers whoconducted some brilliant work and to provide a proof of concept for intelligent content-based image retrieval techniques.

An implementation of optimized framework for action classification using multilayers neural network on selected fused features

AbstractIn video sequences, human action recognition is a challenging problem due to motion variation, in frame person difference, and setting of video recording in the field of computer vision. Since last few years, applications of human activity recognition have increased significantly. In the literature, many techniques are implemented for human action recognition, but still they face problem in contrast of foreground region, segmentation, feature extraction, and feature selection. This article contributes a novel human action recognition method by embedding the proposed frames fusion working on the principle of pixels similarity. An improved hybrid feature extraction increases the recognition rate and allows efficient classification in the complex environment. The design consists of four phases, (a) enhancement of video frames (b) threshold-based background subtraction and construction of saliency map (c) feature extraction and selection (d) neural network (NN) for human action classification. Results have been tested using five benchmark datasets including Weizmann, KTH, UIUC, Muhavi, and WVU and obtaining recognition rate 97.2, 99.8, 99.4, 99.9, and 99.9%, respectively. Contingency table and graphical curves support our claims. Comparison with existent techniques identifies the recognition rate and trueness of our proposed method.

A NOVEL BIOMECHANICS-BASED APPROACH FOR PERSON RE-IDENTIFICATION BY GENERATING DENSE COLOR SIFT SALIENCE FEATURES

Currently, identifying humans using biomechanics-based approaches has gained a lot of significance for person re-identification. Biomechanics-based approaches use knee-hip angle–angle relationships and body movements for person re-identification. Generally, biomechanics of human walking and running is used for person re-identification. In fact, person re-identification is a complex and important task in academia as well as industry and remains an unsolved issue in the computer vision field. The subjects most commonly addressed regarding person re-identification include significant feature extraction that can function accurately with invariant appearance and robust classification. In this study, a significant color feature descriptor is proposed by combining dense color-SIFT and global convex hull salience region features. First convex hull boundary points are detected using the SIFT technique. Furthermore, it is extended with Grubb’s outlier test to eliminate the outlier points detected by SIFT and mark t...

Computer-based classification of chromoendoscopy images using homogeneous texture descriptors

Computer-aided analysis of clinical pathologies is a challenging task in the field of medical imaging. Specifically, the detection of abnormal regions in the frames collected during an endoscopic session is difficult. The variations in the conditions of image acquisition, such as field of view or illumination modification, make it more demanding. Therefore, the design of a computer-assisted diagnostic system for the recognition of gastric abnormalities requires features that are robust to scale, rotation, and illumination variations of the images. Therefore, this study focuses on designing a set of texture descriptors based on the Gabor wavelets that will cope with certain image dynamics. The proposed features are extracted from the images and utilized for the classification of the chromoendoscopy (CH) frames into normal and abnormal categories. Moreover, to attain a higher accuracy, an optimized subset of descriptors is selected through the genetic algorithm. The results obtained using the proposed features are compared with other existing texture descriptors (e.g., local binary pattern and homogeneous texture descriptors). Furthermore, the selected features are used to train the support vector machine (SVM), naive Bayes (NB) algorithm, k-nearest neighbor algorithm, linear discriminant analysis, and ensemble tree classifier. The performance of these state-of-the-art classifiers for different texture descriptors is compared based on the accuracy, sensitivity, specificity, and area under the curve (AUC) derived by using the CH images. The classification results reveal that the SVM classifier achieves 90.0% average accuracy and 0.93 AUC when it is employed with an optimized set of features obtained by using a genetic algorithm.

A Review on Recent Developments for Detection of Diabetic Retinopathy

Diabetic retinopathy is caused by the retinal micro vasculature which may be formed as a result of diabetes mellitus. Blindness may appear as a result of unchecked and severe cases of diabetic retinopathy. Manual inspection of fundus images to check morphological changes in microaneurysms, exudates, blood vessels, hemorrhages, and macula is a very time-consuming and tedious work. It can be made easily with the help of computer-aided system and intervariability for the observer. In this paper, several techniques for detecting microaneurysms, hemorrhages, and exudates are discussed for ultimate detection of nonproliferative diabetic retinopathy. Blood vessels detection techniques are also discussed for the diagnosis of proliferative diabetic retinopathy. Furthermore, the paper elaborates a discussion on the experiments accessed by authors for the detection of diabetic retinopathy. This work will be helpful for the researchers and technical persons who want to utilize the ongoing research in this area.

An Efficient Content Based Image Retrieval using EI Classification and Color Features

An efficient method for image search and retrieval has been proposed in this study. For this purpose images aredecomposed in equal squares of minimum 24x16 size and then edge detection is applied to those decomposed parts.Pixels classification is done on the basis of edge pixels and inner pixels. Features are selected from edge pixels forpopulating the database. Moreover, color differences are used to cluster same color retrieved results. Precision andrecall rates have been used as quantification measures. It can be seen from the results that proposed method showsa very good balance of precision and recall in minimum retrieval time, achieved results are comprised of 66%-100%rate for precision and 68%-80% for recall.

Powerful Descriptor for Image Retrieval Based on Angle Edge and Histograms

By gaining the place of active and important research area, Content based image retrieval has been proposed ina number of different ways after its inception. In the proposed method, a new angle orientation histogram hasbeen introduced named as Angle Edge Histogram. By applying Pythagorean theory to image, very usefulcharacteristics have been obtained for image matching, search and retrieval. Proposed method has also beencompared with existing methods and the results show that it outperforms the existing methods in values ofprecision and recall and balance of precision and recall. Proposed method receives an average of 94% ofprecision and 79% of recall rates.

License number plate recognition system using entropy-based features selection approach with SVM

License plate recognition (LPR) system plays a vital role in security applications which include road traffic monitoring, street activity monitoring, identification of potential threats, and so on. Numerous methods were adopted for LPR but still, there is enough space for a single standard approach which can be able to deal with all sorts of problems such as light variations, occlusion, and multi-views. The proposed approach is an effort to deal under such conditions by incorporating multiple features extraction and fusion. The proposed architecture is comprised of four primary steps: (i) selection of luminance channel from CIE-Lab colour space, (ii) binary segmentation of selected channel followed by image refinement, (iii) a fusion of Histogram of oriented gradients (HOG) and geometric features followed by a selection of appropriate features using a novel entropy-based method, and (iv) features classification with support vector machine (SVM). To authenticate the results of proposed approach, different performance measures are considered. The selected measures are False positive rate (FPR), False negative rate (FNR), and accuracy which is achieved maximum up to 99.5%. Simulation results reveal that the proposed method performs exceptionally better compared with existing works.

Big data analysis for brain tumor detection: Deep convolutional neural networks

Abstract Brain tumor detection is an active area of research in brain image processing. In this work, a methodology is proposed to segment and classify the brain tumor using magnetic resonance images (MRI). Deep Neural Networks (DNN) based architecture is employed for tumor segmentation. In the proposed model, 07 layers are used for classification that consist of 03 convolutional, 03 ReLU and a softmax layer. First the input MR image is divided into multiple patches and then the center pixel value of each patch is supplied to the DNN. DNN assign labels according to center pixels and perform segmentation. Extensive experiments are performed using eight large scale benchmark datasets including BRATS 2012 (image dataset and synthetic dataset), 2013 (image dataset and synthetic dataset), 2014, 2015 and ISLES (Ischemic stroke lesion segmentation) 2015 and 2017. The results are validated on accuracy (ACC), sensitivity (SE), specificity (SP), Dice Similarity Coefficient (DSC), precision, false positive rate (FPR), true positive rate (TPR) and Jaccard similarity index (JSI) respectively.