Automated tracing of retinal blood vessels using graphical models

As an early indication of diseases including diabetes, hypertension, and retinopathy of prematurity, structural study of retinal vessels becomes increasingly important. These studies have driven the need toward accurate and consistent tracing of retinal blood vessel tree structures from fundus images in an automated manner. In this paper we propose a two-step pipeline: First, the retinal vessels are segmented with the preference of preserving the skeleton network, i.e., retinal segmentation with a high recall. Second, a novel tracing algorithm is developed where the tracing problem is uniquely mapped to an inference problem in probabilistic graphical models. This enables the exploitation of well-developed inference toolkit in graphical models. The competitive performance of our method is verified on publicly available datasets comparing to the state-of-the-arts.