Max-Min Central Vein Detection in Retinal Fundus Images

This paper describes a new framework for the automated tracking of the central retinal vein in retinal images. The procedure first computes a binary image of the retinal vasculature, then obtains the skeleton (medial axis) of the vascular network. Terminal and branching points of the network are then located, and the network converted into a graph representation including length and thickness information for all vessels. Finally, a maxmin approach is used to locate the central vein: the candidates central vein are the minimal paths from the optic disk to all terminal nodes found using Dijkstra algorithm. The actual central vein is selected among the all candidates by maximizing a merit function estimating the total vessel area in the image. Results are presented and compared with those provided by a manual classification on 20 images of the DRIVE set. An overall performance ratio of 92% is achieved.