While many approaches exist to segment retinal vessels in fundus photographs, only a limited number focus on the construction and disambiguation of arterial and venous trees. Previous approaches are local and/or greedy in nature, making them susceptible to errors or limiting their applicability to large vessels. We propose a more global framework to generate arteriovenous trees in retinal images, given a vessel segmentation. In particular, our approach consists of three stages. The first stage is to generate an overconnected vessel network, named the vessel potential connectivity map (VPCM), consisting of vessel segments and the potential connectivity between them. The second stage is to disambiguate the VPCM into multiple anatomical trees, using a graph-based metaheuristic algorithm. The third stage is to classify these trees into arterial or venous (A/V) trees. We evaluated our approach with a ground truth built based on a public database, showing a pixel-wise classification accuracy of 88.15% using a manual vessel segmentation as input, and 86.11% using an automatic vessel segmentation as input.
Bifurcations of retinal vessels in fundus images are important structures clinically and their detection is also an
important component in image processing algorithms such as registration, segmentation and change detection.
In this paper, we develop a method for direct bifurcation detection based on the optimal filter framework. This
approach first generates a set of filters to represent all cases of bifurcations, and then uses them to generate a
feature space for a classifier to distinguish bifurcations and non-bifurcations. This approach is different from
previous methods as it uses a minimal number of assumptions, essentially only requiring training images and
expert annotations of bifurcations. The method is trained on 60 fundus images and tested on 20 fundus images,
resulting in an AUC of 0.883, which compares well to a human expert.