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Simultaneously Identifying All True Vessels From Segmented Retinal Images

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4 Author(s)
Qiangfeng Peter Lau ; Department of Computer Science , National University of Singapore, Singapore ; Mong Li Lee ; Wynne Hsu ; Tien Yin Wong

Measurements of retinal blood vessel morphology have been shown to be related to the risk of cardiovascular diseases. The wrong identification of vessels may result in a large variation of these measurements, leading to a wrong clinical diagnosis. In this paper, we address the problem of automatically identifying true vessels as a postprocessing step to vascular structure segmentation. We model the segmented vascular structure as a vessel segment graph and formulate the problem of identifying vessels as one of finding the optimal forest in the graph given a set of constraints. We design a method to solve this optimization problem and evaluate it on a large real-world dataset of 2446 retinal images. Experiment results are analyzed with respect to actual measurements of vessel morphology. The results show that the proposed approach is able to achieve 98.9% pixel precision and 98.7% recall of the true vessels for clean segmented retinal images, and remains robust even when the segmented image is noisy.

Published in:

IEEE Transactions on Biomedical Engineering  (Volume:60 ,  Issue: 7 )