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Retinal artery-vein classification via topology estimation
- Publication Year :
- 2015
-
Abstract
- 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.
- Subjects :
- Databases, Factual
Retinal Artery
Fundus image
Topology (electrical circuits)
Fundus (eye)
Diagnostic Techniques, Ophthalmological
Network topology
Topology
Article
medicine
Image Processing, Computer-Assisted
Humans
Computer vision
Peripheral vessels
Electrical and Electronic Engineering
Vein
Mathematics
Radiological and Ultrasound Technology
business.industry
Retinal Vein
Computer Science Applications
medicine.anatomical_structure
Artificial intelligence
business
Software
Algorithms
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....e05a10671d9712c9c497d126ee8276ce