Retinal vessel segmentation via Iterative Geodesic Time Transform

Accurate vessel segmentation is the first step in retinal image analysis for medical diagnosis. In this paper we propose a novel method to segment vessel network in fundus image. Vessel centerlines are first extracted by using a set of directional line detectors. Next an Iterative Geodesic Time Transform (ItGTT) is designed to segment the entire vessel network. The idea of the ItGTT is to use centerline pixels as the initial reference set, and compute geodesic time between candidate vessel pixels and the reference set iteratively with an adaptive reference set updating strategy. The entire vessel network is the binary image of the last reference set at the end of the iteration. Experimental results demonstrate that our method can segment retinal vessels effectively.