Automatic detection of optic disc from retinal fundus images using dynamic programming

Automatic detection of optic disc (OD) in fundus images is used to determine potential clinical parameters for diagnosis of retinopathic diseases. Due to the presence of vascular-tree blood vessels, the detection of OD area is a complicated task for a computer-aided diagnosis (CAD) system, desirable by ophthalmologists. In this paper, a novel system for the detection of OD area has been developed. It consists of four major steps: preprocessing with a color space transformation and contrast normalization; segmentation of the vascular-tree through radial projection (RP) and weighted-derivative of Gaussian (WDOG) techniques; feature preserving removal of the detected vessels by a fast marching inpainting algorithm, detection of candidate to OD pixels via dynamic programming and OD area location using ellipse fitting methods. The proposed technique has been tested on 129 retinal images from to public and widely used datasets, DRIVE and DIARETB1. Experiments on this dataset indicate that this algorithm is computationally fast and able to achieve 92.5 % of accuracy for OD detection.