A Robust Algorithm for Optic Disc Segmentation from Colored Fundus Images

Abstract

Efficient and accurate optic disc (OD) segmentation is an essential task in automated diagnosis of different retinal diseases from digital fundus images. Due to presence of non-uniform illumination, noise, vessels and other lesions in the fundus images, it is challenging to come up with an algorithm which can accurately segment the OD from the fundus images. It is even more difficult to detect OD accurately for real time patient data in which the images are not captured in the very control environment. This paper presents a novel approach for efficient and robust OD segmentation even in presence of high retinal pathologies and noise. The proposed system consists of four modules i.e. preprocessing, candidate OD regions detection, vessel segmentation and OD detection based on vessel density in candidate regions. The proposed system is tested and validated on publicly available fundus image databases and images gathered locally for real patients. The experimental results show the validation of proposed system.