Glaucoma diagnosis by means of optic cup feature analysis in color fundus images

Abstract

Glaucoma is an asymptomatic eye disease and one of the major causes of irreversible blindness worldwide. For this reason, there have been significant advances in automatic screening tools for early detection. In this paper, an automatic glaucoma diagnosis algorithm based on retinal fundus image is presented. This algorithm uses anatomical characteristics such as the position of the vessels and the cup within the optic nerve. Using several color spaces and the Stochastic Watershed transformation, different characteristics of the optic nerve were analyzed in order to distinguish between a normal and a glaucomatous fundus. The proposed algorithm was evaluated on 53 images (24 normal and 29 glaucomatous images). The specificity and sensitivity obtained by the proposed algorithm are 0.81 and 0.87 using Luv color space, which means considerable performance in diagnosis systems.