Automated separation of binary overlapping trees in low-contrast color retinal images.

While many approaches exist for the automated segmentation of retinal vessels in fundus photographs, limited work has focused on the problem of separating the arterial from the venous trees. The few existing approaches that do exist for separating arteries from veins are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to only the very largest vessels. In this work, we propose a new, more global, optimization framework for separating two overlapping trees within medical images and apply this approach for the separation of arteriovenous trees in low-contrast color fundus images. In particular, our approach has two stages. The first stage is to generate a vessel potential connectivity map (VPCM) consisting of vessel segments and the potential connectivity between them. The second stage is to separate the VPCM into multiple anatomical trees using a graph-based meta-heuristic algorithm. Based on a graph model, the algorithm first uses local knowledge and global constraints of the vasculature to generate near-optimal candidate solutions, and then obtains the final solution based on global costs. We test the algorithm on 48 low-contrast fundus images and the promising results suggest its applicability and robustness.