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In general, the nerve that links the eye to the brain is affected because of high eye pressure. The most common kind of glaucoma sometimes has no other symptoms than a gradual loss of vision. In this study, the Glaucoma Image Classification (GIC) is made by using different entropy features and Maximum Likelihood Classifier (MLC). Initially, the input fundus images are decomposed by using rankles transform, then the entropy features like sample entropy, Shannon entropy and approximate entropy are used to extract features. Finally, MLC is applied for classification. The GIC scheme’s function produces the classification accuracy of 96 % by using Shannon entropy feature and MLC.

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In this study, a Discriminator Model for Glaucoma Diagnosis (DMGD) using soft computing techniques is presented. As the biomedical images such as fundus images are often acquired in high resolution, the Region of Interest (ROI) for glaucoma diagnosis must be selected at first to reduce the complexity of any system. The DMGD system uses a series of pre-processing; initial cropping by the green channel's intensity, Spatially Weighted Fuzzy C Means (SWFCM), blood vessel detection and removal by Gaussian Derivative Filters (GDF) and inpainting algorithms. Once the ROI has been selected, the numerical features such as colour, spatial domain features from Local Binary Pattern (LBP) and frequency domain features from LAWS are generated from the corresponding ROI for further classification using kernel based Support Vector Machine (SVM). The DMGD system performances are validated using four fundus image databases; ORIGA, RIM-ONE, DRISHTI-GS1, and HRF with four different kernels; Linear Kernel (LK), Polynomial Kernel (PK), Radial Basis Function (RBFK) kernel, Quadratic Kernel (QK) based SVM classifiers. Results show that the DMGD system classifies the fundus images accurately using the multiple features and kernel based classifies from the properly segmented ROI.

The main aim of the paper is to develop an early detection system for glaucoma classification using the fundus images. By reviewing the various glaucoma image classification schemes, suitable features and supervised approaches are identified. An automated Computer Aided Diagnosis (CAD) system is developed for glaucoma based on soft computing techniques. It consists of three stages. The Region Of Interest (ROI) is selected in the first stage that comprises of Optic Disc (OD) region only. It is selected automatically based on the on the green channel’s highest intensity. In the second stage, features such as colour and Local Binary patterns (LBP) are extracted. In the final stage, classification of fundus image is achieved by employing supervised learning of Support Vector Machine (SVM) classifier for classifying the fundus images into either normal or glaucomatous. The evaluation of the CAD system on four public databases; ORIGA, RIM-ONE, DRISHTI-GS, and HRF show that LBP gives promising results than the conventional colour features.