TY - GEN
T1 - Retinal artery and vein classification via dominant sets clustering-based vascular topology estimation
AU - Zhao, Yitian
AU - Xie, Jianyang
AU - Su, Pan
AU - Zheng, Yalin
AU - Liu, Yonghuai
AU - Cheng, Jun
AU - Liu, Jiang
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Artery/vein classification
KW - Dominant sets
KW - Topology
KW - Vessel
UR - http://www.scopus.com/inward/record.url?scp=85054059692&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00934-2_7
DO - 10.1007/978-3-030-00934-2_7
M3 - Conference contribution
AN - SCOPUS:85054059692
SN - 9783030009335
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 56
EP - 64
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 - 21st International Conference, 2018, Proceedings
A2 - Fichtinger, Gabor
A2 - Davatzikos, Christos
A2 - Alberola-López, Carlos
A2 - Frangi, Alejandro F.
A2 - Schnabel, Julia A.
PB - Springer Verlag
T2 - 21st International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2018
Y2 - 16 September 2018 through 20 September 2018
ER -