Automated Glaucoma Detection Using Hybrid Feature Extraction in Retinal Fundus Images

Abstract

Glaucoma is one of the most common causes of blindness. Robust mass screening may help to extend the symptom-free life for affected patients. To realize mass screening requires a cost effective glaucoma detection method which integrates well with digital medical and administrative processes. To address these requirements, we propose a novel low cost automated glaucoma diagnosis system based on hybrid feature extraction from digital fundus images. The paper discusses a system for the automated identification of normal and glaucoma classes using Higher Order Spectra (HOS), Trace Transform (TT) and Discrete Wavelet Transform (DWT) features. The extracted features are fed to a Support Vector Machine (SVM) classifier with linear, polynomial order 1, 2, 3 and Radial Basis Function (RBF) in order to select the best kernel for automated decision making. In this work, the SVM classifier, with a polynomial order 2 kernel function, was able to identify glaucoma and normal images with an accuracy of 91.67%, sensitivity and specificity of 90% and 93.33% respectively. Furthermore, we propose a novel integrated index called Glaucoma Risk Index (GRI) which is composed from HOS, TT and DWT features, to diagnose the unknown class using a single feature. We hope that this GRI will aid clinicians to make a faster glaucoma diagnosis during mass screening of normal/glaucoma images.

Performance Analysis of Gray Level Co-Occurrence Matrix Texture Features for Glaucoma Diagnosis

“…The texture descriptors that contributed notably to distinguish lesions were contrast and correlation. Krishnan and Oliver (2013) proposed an automated glaucoma diagnosis system based on hybrid feature extraction from digital fundus images. Further a novel integrated index called Glaucoma Risk Index (GRI) which is composed from HOS, TT and DWT features, is used to diagnose the unknown class using a single feature during the mass screening of normal/glaucoma images.…”

Development of a quantitative intracranial vascular features extraction tool on 3DMRA using semiautomated open‐curve active contour vessel tracing

“…In this study, we aimed to develop a comprehensive semiautomated intracranial artery feature-extraction technique using improved approaches for automated vessel tracing and labeling. Automated vessel tracing, which converts raw pixel or voxel content in planar or volumetric images to a topological and geometrical network with centerline and radius, is the most important step for vascular feature extraction, as demonstrated from applications of retinal fundus images (14,15) and CT coronary angiography (16,17). Tracing by using the active contour model (snake) allows one to handle changes of topology and adapt locally to the shape of complex structures (18).…”

Glaucoma Detection Technique in Retina-A study

“…xi. Krishnan et al [23] proposed a method in which they used the texture features for the detection of Glaucoma. In this method, classification accuracy of 91.67% was obtained using the SVM classifier.…”