Retinal Artery-Vein Classification via Topology Estimation

• Published in: IEEE transactions on medical imaging Vol. 34; no. 12; pp. 2518 - 2534
• Main Authors: Estrada, Rolando, Allingham, Michael J., Mettu, Priyatham S., Cousins, Scott W., Tomasi, Carlo, Farsiu, Sina
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Arteries; Artery-vein classification; Databases, Factual; Diagnostic Techniques, Ophthalmological; graph theory; Humans; image analysis; Image color analysis; Image Processing, Computer-Assisted - methods; Labeling; medical imaging; Retina; Retinal Artery - anatomy & histology; Retinal Vein - anatomy & histology; Space exploration; Topology; tree topology; Veins
• ISSN: 0278-0062; 1558-254X
• DOI: 10.1109/TMI.2015.2443117

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.