Image-Based Risk Assessment Analysis for Glaucoma Determination

ICTACT Journal on Image and Video Processing

A Glaucoma is a group of eye diseases causing optic nerve damage and if not detected at an early stage it may cause permanent blindness. Glaucoma progression precedes some structural damage to the retina are the symptoms of Glaucoma. Manually, it is diagnosed by examination of size, structure, shape, and color of optic disc and optic cup and retinal nerve fiber layer (RNFL), which suffer from the subjectivity of human due to experience, fatigue factor etc., and with the widespread of higher quality medical imaging techniques, there are increasing demands for computer-aided diagnosis (CAD) systems for glaucoma detection, because the human mistakes, other retinal diseases like Age-related Macular Degeneration (AMD) affecting in early glaucoma detection, and the existing medical devices like Optical Coherence Tomography (OCT) and Heidelberg Retinal Tomography (HRT) are expensive. This paper proposes a novel algorithm by extract 13 shape features from disc and cup, extract 25 texture features from RNFL(retinal nerve fiber layer) using gray level co-occurrence method and Tamara algorithm and 3 color features for each of disc and cup and RNFL. Next, best features selected using two methods, first method is the student t-test and the second method applied was the Sequential Feature Selection (SFS) to introduce the best 6 features. The evaluation of proposed algorithm is performed using a RIM_ONE and DRISHTI-GS databases, the average accuracy 97%, maximize area under curve (AUC) 0.99, specificity 96.6% and sensitivity 98.4% using support vector machine classifier (SVM). Future works suggested to design a complete, automated system not just diagnose glaucoma but calculate the progress of the disease too.

2018

Morphological shape of the optic nerve's disc and excavation presents an important feature in the identification of eyes' diseases such as glaucoma. Leading to the optic nerve head (ONH) destruction, this sickness is considered as the second leading cause of blindness worldwide and mainly in least developed countries. Since early detection is crucial to cure this disease, this paper describes a new decision-making system based on Artificial Neural Network (ANN) classifier. The suggested method has the advantage of taking into consideration both instrumental parameters (Cup-to-Disc Ratio, ISNT rule and eyes' asymmetry) and factor risks (age, gender, genetic history and origin). Experiments are performed on a real dataset of ophthalmologic images of normal and glaucomatous cases. The experimental results show high accuracy compared with some existing systems.

2021

Glaucoma is a disease that affects the optic nerve. This disease, over a period of time, can lead to loss of vision. It is also known as 'silent thief of sight'. This is because this disease slowly damages the eye, and ultimately causes irreparable harm before any vision loss. There are several methods in which the disease can be treated, if detected at an early stage. It is definitely not possible for any technology, including artificial intelligence, to replace a doctor. However, it is possible to develop a model based on several classical image processing algorithms, combined with artificial intelligence that can detect onset of Glaucoma based on certain parameters of the retinal fundus. This model would play an important role in early detection of the disease and assist the doctor. The traditional methods to detect glaucoma, as efficient as they may be, are usually expensive. Here we propose a machine learning approach to diagnose from fundus images and accurately classify its severity. In this paper we propose Support Vector Machine (SVM) method to segregate, train the models using a high-end graphics processor unit (GPU) and augmented the hull convex approach to boost the accuracy of the image processing mechanisms along with distinguishing the different stages of glaucoma. Added to these, we have proposed a feasible web application for the screening process.

IEEE Access, 2021

Glaucoma is an incurable eye disease that leads to slow progressive degeneration of the retina. It cannot be fully cured, however, its progression can be controlled in case of early diagnosis. Unfortunately, due to the absence of clear symptoms during the early stages, early diagnosis are rare. Glaucoma must be detected at early stages since late diagnosis can lead to permanent vision loss. Glaucoma affects the retina by damaging the Optic Nerve Head (ONH). Its diagnosis is dependent on the measurements of Optic Cup (OC) and Optic Disc (OD) in the retina. Computer vision techniques have been shown to diagnose glaucoma effectively and correctly with little overhead. These techniques measure OC and OC dimensions using machine learning based classification and segmentation algorithms. This article aims to provide a comprehensive overview of various existing techniques that use machine learning to detect and diagnose glaucoma based on fundus images. Readers would be able to understand the challenges glaucoma presents from an image processing and machine learning standpoint and will be able to identify gaps in current research. INDEX TERMS Glaucoma, convolutional neural networks (CNN), diabetic retinopathy, cup-to-disc ratio (CDR), optic nerve head (ONH), optic cup (OC), optic disc (OD), intra ocular pressure (IOP).

2019

Glaucoma is considered the second cause of blindness worldwide. It damages the optic nerve causing irreversible blindness if it doesn't be early detected. This paper aims to detect and classify Glaucoma. It adopts the Grey Level Co-occurrence Matrix (GLCM) and GrayLevel Run Length Matrix (GLRLM) methods to extract 29 statistical texture features. Then, the artificial neural network (ANN) is trained with the back propagation technique for classification. MATLAB is used for image processing and computation. Accuracy is found to be 99% which is one of the highest levels compared with the existing research.

مجلة العلوم البحثة والتطبيقية, 2021

Engineering, Technology & Applied Science Research, 2025

Significant advances in the automated glaucoma detection techniques have been made through the employment of the Machine Learning (ML) and Deep Learning (DL) methods, an overview of which will be provided in this paper. What sets the current literature review apart is its exclusive focus on the aforementioned techniques for glaucoma detection using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines for filtering the selected papers. To achieve this, an advanced search was conducted in the Scopus database, specifically looking for research papers published in 2023, with the keywords "glaucoma detection", "machine learning", and "deep learning". Among the multiple found papers, the ones focusing on ML and DL techniques were selected. The best performance metrics obtained using ML recorded in the reviewed papers, were for the SVM, which achieved accuracies of 98.31%, 98.61%, 96.43%, 96.67%, 95.24%, and 98.60% in the ACRIMA, REFUGE, RIM-ONE, ORIGA-light, DRISHTI-GS, and sjchoi86-HRF databases, respectively, employing the REFUGE-trained model, while when deploying the ACRIMA-trained model, it attained accuracies of 98.92%, 99.06%, 98.27%, 97.10%, 96.97%, and 96.36%, in the same databases, respectively. The best performance metrics obtained utilizing DL recorded in the reviewed papers, were for the lightweight CNN, with an accuracy of 99.67% in the Diabetic Retinopathy (DR) and 96.5% in the Glaucoma (GL) databases. In the context of non-healthy screening, CNN achieved an accuracy of 99.03% when distinguishing between GL and DR cases. Finally, the best performance metrics were obtained using ensemble learning methods, which achieved an accuracy of 100%, specificity of 100%, and sensitivity of 100%. The current review offers valuable insights for clinicians and summarizes the recent techniques used by the ML and DL for glaucoma detection, including algorithms, databases, and evaluation criteria.

Innovation in Medicine and Healthcare, 2020

Glaucoma is the second leading cause of blindness globally, it is characterized by degeneration of the optic nerve with particular patterns of corresponding defects in the visual field. Aiding doctors in early diagnosis and detection of progression is crucial, as glaucoma is asymptomatic in nature. Furthermore there is good therapeutic results in early cases before irreversible visual loss occurs. Thus it is of great importance to find automated methods to discriminate glaucomatous diseases giving insight to doctors. In order to develop a Computer Aided Diagnosis system (CAD), we realised an extensive competitive study of pattern recognition methods should be undertaken. A range of methods have been evaluated including the use of Deep Neural Networks (DNN), Support Vector Machines (SVM), Decision Trees (DT) and K-Nearest Neighbours (KNN) for diagnosing glaucoma. Using a range of classification techniques, this paper aims to diagnose glaucomatous diseases. Results have been produced with data comprising of Visual Field and OCT Disc readings from anonymous patients with and without glaucoma. Multiple systems are proposed that can predict diagnosis for ocular hypertension, primary open angle glaucoma, normal tension glaucoma and healthy patients with a reasonable confidence. Best performance has been obtained from voting classier comprised of SVM and KNN at 0.87 (AUC) and DNN at 0.87 (AUC) which possibly could be used as an automatic diagnosis aid in order to streamline the diagnosis of glaucoma for complex cases or flagging of urgent cases.

International Journal for Research in Applied Science and Engineering Technology, 2018

Glaucoma is one of the principal causes of blindness in the world 1. It is a condition of eye disease which leads to irreversible blindness in advanced stages. Early diagnosis is an important objective in Glaucoma diagnosis to maintain the best visual acuity throughout the life of the people with Glaucoma. A Neural Network approach is proposed for the diagnosis of Glaucoma. Automated analysis of information from various diagnostic techniques was performed to improve Glaucoma detection in the clinic. This paper discusses the inclusion of neural networks in Glaucoma diagnosis. Data from clinical examination, pachymetry, perimetry and analysis of the retinal nerve fiber layer were integrated in a system of Artificial Intelligence. Analysis of 106 eyes obtained from practicing ophthalmologist, which represent various stages of glaucoma is used to develop an ANN. In Multilayer perceptron, the learning was carried out with 66% of the data and with the training function of gradient descent with momentum backpropagation. The classifier accuracy was. This method provides an efficient and accurate tool for the diagnosis of Glaucoma in the stages of glaucomatous illness by means of ANN.

SpringerPlus, 2016

Background Glaucoma is a chronic ocular disorder which can cause blindness if left undetected at an early stage. The World Health Organization has declared Glaucoma to be the second largest cause of blindness all over the world and it encompasses 15 % of the blindness cases in the world which makes 5.2 million of the world's population (Thylefors and Negrel 1994) and the number is expected to increase up to 80 million by 2020 (Quigley and Broman 2006). Glaucoma originates from increase in intraocular pressure caused by aqueous humor, a fluid produced by the eye. In normal eye, a balance is maintained as the amount of liquid produced is equal to the amount of liquid discharged by the eye. However, in glaucoma the liquid do not flow out of the eye and increases stress on the eye resulting in the damage of optic nerve which is responsible for brain and eye communication. Increase in pressure with time results in severe destruction to optic nerve and may ends up with irreversible blindness (http://www.geteyesmart.org/eyesmart/diseases/glaucoma/). Increase in intraocular pressure (IOP) causes no early symptoms or