Vessel segmentation in retinal images using graph-theoretical vessel tracking

Abstract

This paper presents a method for automatic segmentation of blood vessels in retinal images. The method is based on vessel tracking technique. The key idea of the method is that first a set of seed points (center of vessel cross sections) is extracted. Then, the seed points are connected to establish the vessel skeleton. Finally, the false vessel point are rejected by resorting to a hypothesis-verificaton based procedure. The major contribution of this work is that we formulate the step of seed point connection in the form of graph-theoretical shortest path problem. Then we apply the Dijkstra's algorithm to solve the problem. The performance of our method evaluated on the publicly available DRIVE database shows promising results.