Retinal Artery-Vein Classification via Topology Estimation.


Journal Article

We propose a novel, graph-theoretic framework for distinguishing arteries from veins in a fundus image. We make use of the underlying vessel topology to better classify small and midsized vessels. We extend our previously proposed tree topology estimation framework by incorporating expert, domain-specific features to construct a simple, yet powerful global likelihood model. We efficiently maximize this model by iteratively exploring the space of possible solutions consistent with the projected vessels. We tested our method on four retinal datasets and achieved classification accuracies of 91.0%, 93.5%, 91.7%, and 90.9%, outperforming existing methods. Our results show the effectiveness of our approach, which is capable of analyzing the entire vasculature, including peripheral vessels, in wide field-of-view fundus photographs. This topology-based method is a potentially important tool for diagnosing diseases with retinal vascular manifestation.

Full Text

Duke Authors

Cited Authors

  • Estrada, R; Allingham, MJ; Mettu, PS; Cousins, SW; Tomasi, C; Farsiu, S

Published Date

  • December 2015

Published In

Volume / Issue

  • 34 / 12

Start / End Page

  • 2518 - 2534

PubMed ID

  • 26068204

Pubmed Central ID

  • 26068204

Electronic International Standard Serial Number (EISSN)

  • 1558-254X

Digital Object Identifier (DOI)

  • 10.1109/TMI.2015.2443117


  • eng

Conference Location

  • United States