Automatic Segmentation of Optic Disc Using Affine Snakes in Gradient Vector Field

The optic disc is one of the prominent features of a retinal fundus image, and its segmentation is a critical component in automated retinal screening systems for ophthalmic anomalies, such as diabetic retinopathy and glaucoma. In this paper, we propose a novel method for optic disc segmentation using affine snakes, where the snake evolves using an affine transformation and requires a priori knowledge of the desired object shape. We determine the affine transformation parameters by first computing a force field on the image and then deforming the snake till the net force on the snake is zero. The affine snakes technique excels in its speed of convergence. This is attributed to the fact that only six parameters require optimization, the six parameters being the horizontal and vertical scaling, shearing and translation components of an affine transformation. Localization of the optic disc is done using normalized cross-correlation and segmentation is done using the affine snakes technique. This technique is tested on publicly available fundus image datasets, such as IDRiD, Drishti-GS, RIM-ONE, DRIONS-DB, and Messidor, with Dice In-dices of 0.943, 0.958, 0.933, 0.913, and 0.912, respectively.