Retinal artery and vein classification via dominant sets clustering based vascular topology estimation



Zhao, Yitian, Xie, Jianyang ORCID: 0000-0002-4565-5807, Su, Pan, Zheng, Y ORCID: 0000-0002-7873-0922, Liu, Yonghuai, Cheng, Jun and Liu, Jiang
(2018) Retinal artery and vein classification via dominant sets clustering based vascular topology estimation. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), 2018-9-16 - 2018-9-20, Granada, Spain.

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Abstract

The classification of the retinal vascular tree into arteries and veins is important in understanding the relation between vascular changes and a wide spectrum of diseases. In this paper, we have proposed a novel framework that is capable of making the artery/vein (A/V) distinction in retinal color fundus images. We have successfully adapted the concept of dominant sets clustering and formalize the retinal vessel topology estimation and the A/V classification problem as a pairwise clustering problem. Dominant sets clustering is a graph-theoretic approach that has been proven to work well in data clustering. The proposed approach has been applied to three public databases (INSPIRE, DRIVE and VICAVR) and achieved high accuracies of 91.0%, 91.2%, and 91.0%, respectively. Furthermore, we have made manual annotations of vessel topologies from these databases, and this annotation will be released for public access to facilitate other researchers in the community to do research in the same and related topics.

Item Type: Conference or Workshop Item (Unspecified)
Uncontrolled Keywords: Artery/vein classification, Dominant sets, Vessel, Topology
Depositing User: Symplectic Admin
Date Deposited: 21 Jun 2018 09:19
Last Modified: 19 Jan 2023 01:31
DOI: 10.1007/978-3-030-00934-2_7
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3022868