Simultaneously identifying all true vessels from segmented retinal images.

Published

Journal Article

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 2,446 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.

Full Text

Duke Authors

Cited Authors

  • Lau, QP; Lee, ML; Hsu, W; Wong, TY

Published Date

  • July 2013

Published In

Volume / Issue

  • 60 / 7

Start / End Page

  • 1851 - 1858

PubMed ID

  • 23372070

Pubmed Central ID

  • 23372070

Electronic International Standard Serial Number (EISSN)

  • 1558-2531

International Standard Serial Number (ISSN)

  • 0018-9294

Digital Object Identifier (DOI)

  • 10.1109/tbme.2013.2243447

Language

  • eng