Retinal Artery and Vein Classification via Dominant Sets Clustering-Based Vascular Topology Estimation

  • Yitian ZhaoEmail author
  • Jianyang Xie
  • Pan Su
  • Yalin Zheng
  • Yonghuai Liu
  • Jun Cheng
  • Jiang Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


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.


Artery/vein classification Dominant sets Vessel Topology 



This work was supported National Natural Science Foundation of China (61601029, 61602322), Grant of Ningbo 3315 Innovation Team, and China Association for Science and Technology (2016QNRC001).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yitian Zhao
    • 1
    Email author
  • Jianyang Xie
    • 1
    • 2
  • Pan Su
    • 3
  • Yalin Zheng
    • 4
  • Yonghuai Liu
    • 5
  • Jun Cheng
    • 1
  • Jiang Liu
    • 1
  1. 1.Cixi Institute of Biomedical Engineering, Ningbo Institute of Industrial TechnologyChinese Academy of SciencesCixiChina
  2. 2.School of Optics and ElectronicsBeijing Institute of TechnologyBeijingChina
  3. 3.School of Control and Computer EngineeringNorth China Electric Power UniversityBaodingChina
  4. 4.Department of Eye and Vision ScienceLiverpool UniversityLiverpoolUK
  5. 5.Department of Computer ScienceAberystwyth UniversityAberystwythUK

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