Paper Info
Title
Retinal Artery and Vein Classification via Dominant Sets Clustering-Based Vascular Topology Estimation.
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.
Year
DOI
Venue
2018
10.1007/978-3-030-00934-2_7
Lecture Notes in Computer Science
Keywords
Field
DocType
Artery/vein classification,Dominant sets,Vessel,Topology
Public access,Topology,Pairwise comparison,Annotation,Pattern recognition,Computer science,Vein,Retinal Artery,Network topology,Artificial intelligence,Cluster analysis
Conference
Volume
ISSN
Citations 
11071
0302-9743
2
PageRank 
References 
Authors
0.37
10
7
Name
Order
Citations
PageRank
Yitian Zhao124633.15
Jianyang Xie2174.33
Pan Su38211.72
Yalin Zheng426434.69
Yonghuai Liu567561.65
Jun Cheng621420.65
Jiang Liu733534.30