Automated Glaucoma Detection from Fundus Images of Eye Using Statistical Feature Extraction Methods and Support Vector Machine Classification
01 Jan 2018-Vol. 11, pp 511-521
TL;DR: This paper presents a novel technique to diagnose glaucoma using digital fundus images to apply image processing and machine-learning techniques on the digital Fundus images of the eye for separatingglaucomatous eye from normal eye.
Abstract: Glaucoma is one of the eye diseases that can lead to the blindness if not detected and treated at proper time. This paper presents a novel technique to diagnose glaucoma using digital fundus images. In this proposed method, the objective is to apply image processing and machine-learning techniques on the digital fundus images of the eye for separating glaucomatous eye from normal eye. Image preprocessing, techniques such as noise removal and contrast enhancement are used for improving the quality of image thus making it suitable for further processing. Statistical feature extraction methods such as Gray-Level Run Length Matrix (GLRLM) and Gray-Level Co-occurrence Matrix (GLCM) are used for extracting texture features from preprocessed fundus images. Support Vector Machine (SVM) classification method is used for distinguishing glaucomatous eye fundus images from normal, unaffected eye fundus images. The performance of the trained SVM classifier is also tested on a test set of eye fundus images and comparison is done with other existing recent methods of Glaucoma detection.
Citations
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TL;DR: An automated disease localization and segmentation approach based on Fast Region-based Convolutional Neural Network (FRCNN) algorithm with fuzzy k-means (FKM) clustering is presented and a rigorous comparison against the latest methods confirms the efficacy of the approach in terms of both disease detection and segmentsation.
Abstract: Diabetic patients are at the risk of developing different eye diseases i.e., diabetic retinopathy (DR), diabetic macular edema (DME) and glaucoma. DR is an eye disease that harms the retina and DME is developed by the accumulation of fluid in the macula, while glaucoma damages the optic disk and causes vision loss in advanced stages. However, due to slow progression, the disease shows few signs in early stages, hence making disease detection a difficult task. Therefore, a fully automated system is required to support the detection and screening process at early stages. In this paper, an automated disease localization and segmentation approach based on Fast Region-based Convolutional Neural Network (FRCNN) algorithm with fuzzy k-means (FKM) clustering is presented. The FRCNN is an object detection approach that requires the bounding-box annotations to work; however, datasets do not provide them, therefore, we have generated these annotations through ground-truths. Afterward, FRCNN is trained over the annotated images for localization that are then segmented-out through FKM clustering. The segmented regions are then compared against the ground-truths through intersection-over-union operations. For performance evaluation, we used the Diaretdb1, MESSIDOR, ORIGA, DR-HAGIS, and HRF datasets. A rigorous comparison against the latest methods confirms the efficacy of the approach in terms of both disease detection and segmentation.
42 citations
TL;DR: The proposed wavelet-based glaucoma detection algorithm is shown to be suitable for real-time applications as it requires less than 3 s for processing the high-resolution retinal images.
Abstract: Glaucoma is a silent progressive eye disease that is among the leading causes of irreversible blindness. Early detection and proper treatment of glaucoma can limit severe vision impairments associated with advanced stages of the disease. Periodic automatic screening can help in the early detection of glaucoma while reducing the workload on expert ophthalmologists. In this work, a wavelet-based glaucoma detection algorithm is proposed for real-time screening systems. A combination of wavelet-based statistical and textural features computed from the detected optic disc region is used to determine whether a retinal image is healthy or glaucomatous. Two public datasets having different resolutions were considered in the performance analysis of the proposed algorithm. An accuracy of 96.7% and area under receiver operating curve (AUC) of 94.7% were achieved for the high-resolution dataset. Analysis of the wavelet-based statistical and textural features using three different methods showed their relevance for glaucoma detection. Furthermore, the proposed algorithm is shown to be suitable for real-time applications as it requires less than 3 s for processing the high-resolution retinal images.
24 citations
TL;DR: The detection of glaucoma using various learning models based on retinal images is discussed and future research in this research domain is also discussed based on learning models.
Abstract: In the recent world, artificial intelligence (AI) based learning models are widely used in various applications for medical image analysis. These models based on machine learning. The deep study is implemented for the solution of problems like disease identification and classifying various types of medical images. The detection of glaucoma-related eye disease is a major concern for avoiding early blindness and diagnosis of diabetic effect on the eye. There were many models implemented for the detection of glaucoma-related eye disease. In this paper, various existing models along with its performance are discussed to detect eye disease. This paper discusses the detection of glaucoma using various learning models based on retinal images. Further, future research in this research domain is also discussed based on learning models.
17 citations
TL;DR: Improved techniques to extract disease-related and image-based features and machine learning algorithms to support early and automatic diagnosis of Glaucoma symptoms so as to take protective measures and to extend symptom-free life of patients are proposed.
10 citations
TL;DR: The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease.
Abstract: A difficult challenge in the realm of biomedical engineering is the detection of physiological changes occurring inside the human body, which is a difficult undertaking. At the moment, these irregularities are graded manually, which is very difficult, time-consuming, and tiresome due to the many complexities associated with the methods involved in their identification. In order to identify illnesses at an early stage, the use of computer-assisted diagnostics has acquired increased attention as a result of the requirement of a disease detection system. The major goal of this proposed work is to build a computer-aided design (CAD) system to help in the early identification of glaucoma as well as the screening and treatment of the disease. The fundus camera is the most affordable image analysis modality available, and it meets the financial needs of the general public. The extraction of structural characteristics from the segmented optic disc and the segmented optic cup may be used to characterize glaucoma and determine its severity. For this study, the primary goal is to estimate the potential of the image analysis model for the early identification and diagnosis of glaucoma, as well as for the evaluation of ocular disorders. The suggested CAD system would aid the ophthalmologist in the diagnosis of ocular illnesses by providing a second opinion as a judgment made by human specialists in a controlled environment. An ensemble-based deep learning model for the identification and diagnosis of glaucoma is in its early stages now. This method's initial module is an ensemble-based deep learning model for glaucoma diagnosis, which is the first of its kind ever developed. It was decided to use three pretrained convolutional neural networks for the categorization of glaucoma. These networks included the residual network (ResNet), the visual geometry group network (VGGNet), and the GoogLeNet. It was necessary to use five different data sets in order to determine how well the proposed algorithm performed. These data sets included the DRISHTI-GS, the Optic Nerve Segmentation Database (DRIONS-DB), and the High-Resolution Fundus (HRF). Accuracy of 91.11% for the PSGIMSR data set and the sensitivity of 85.55% and specificity of 95.20% for the suggested ensemble architecture on the PSGIMSR data set were achieved. Similarly, accuracy rates of 95.63%, 98.67%, 95.64%, and 88.96% were achieved using the DRIONS-DB, HRF, DRISHTI-GS, and combined data sets, respectively.
8 citations
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Journal Article•
TL;DR: The proposed GLCM based face recognition system not only outperforms well-known techniques such as principal component analysis and linear discriminant analysis, but also has comparable performance with local binary patterns and Gabor wavelets.
Abstract: In this paper, a new face recognition technique is introduced based on the gray-level co-occurrence matrix (GLCM). GLCM represents the distributions of the intensities and the information about relative positions of neighboring pixels of an image. We proposed two methods to extract feature vectors using GLCM for face classification. The first method extracts the well-known Haralick features from the GLCM, and the second method directly uses GLCM by converting the matrix into a vector that can be used in the classification process. The results demonstrate that the second method, which uses GLCM directly, is superior to the first method that uses the feature vector containing the statistical Haralick features in both nearest neighbor and neural networks classifiers. The proposed GLCM based face recognition system not only outperforms well-known techniques such as principal component analysis and linear discriminant analysis, but also has comparable performance with local binary patterns and Gabor wavelets.
154 citations