Abstract— In this paper, an efficient approach for glaucomatous image classification system using fundus images is proposed. The main aim of this study is to detect glaucoma accurately in order to reduce the visual loss and impairment. The proposed system uses two important texture features; Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) in an efficient manner. These texture features are extracted not only from the fundus image but also the optical density image obtained from the fundus image. Before extracting features, region of interest is obtained from the Green channel of the fundus image as it has high contrast than other two colour components. Support Vector Machine (SVM) classifier is used for the classification of fundus image into normal or abnormal based on the extracted features. Results show that the proposed system provides promising results with 100% sensitivity and 99% specificity.
Keywords— Glaucoma, fundus image, optical density, GLCM, LBP, SVM classifier.
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