Retinal Vessel Axis Estimation through a Multi-Directional Graph Search Approach

摘要

The analysis of blood vessels in images of retinal fundus is an important non-invasive procedure that allows early diagnosis and the effective monitoring of therapies in retinopathy. In order to derive a quantitative evaluation of the clinical features, such as vessel diameter and tortuosity, an accurate segmentation of the vessel network has to be performed. A new system for the automatic extraction of the vascular structure in retinal images is proposed. It is based on a sparse tracking technique via a multi-directional graph search approach. We consider the image as a weighted un-oriented graph with arches connecting adjacent pixels and assume that vessels are minimum cost paths connecting remote nodes. An initial seed-finding algorithm based on fast 1-dimensional multi-scale matched filters is run over a regular grid. Simultaneous best-first search graph explorations start from each seed: when two search frontiers meet, the computed shortest path is recorded and exploited for a new search starting from it. New paths are found by iterating the procedure, until the entire vessel network is reconstructed. Lastly, in order to cover the unexplored region with low-contrast vessels, a custom fixing procedure is run. 20 images have been used to test the algorithm, comparing the results with ground-truth manual segmentation. The method provides an average sensitivity of 96.2%.