Retinal Vascular Network Topology Reconstruction and Artery/Vein Classification via Dominant Set Clustering

Yitian Zhao, Yonghuai Liu, Jianyang Xie, Huaizhong Zhang, Yalin Zheng, Yifan Zhao, Hong Qi, Yangchun Zhao, Pan Su, Jiang Liu

Research output: Journal PublicationArticlepeer-review

23 Citations (Scopus)

Abstract

The estimation of vascular network topology in complex networks is important in understanding the relationship between vascular changes and a wide spectrum of diseases. Automatic classification of the retinal vascular trees into arteries and veins is of direct assistance to the ophthalmologist in terms of diagnosis and treatment of eye disease. However, it is challenging due to their projective ambiguity and subtle changes in appearance, contrast, and geometry in the imaging process. In this paper, we propose a novel method that is capable of making the artery/vein (A/V) distinction in retinal color fundus images based on vascular network topological properties. To this end, we adapt the concept of dominant set clustering and formalize the retinal blood vessel topology estimation and the A/V classification as a pairwise clustering problem. The graph is constructed through image segmentation, skeletonization, and identification of significant nodes. The edge weight is defined as the inverse Euclidean distance between its two end points in the feature space of intensity, orientation, curvature, diameter, and entropy. The reconstructed vascular network is classified into arteries and veins based on their intensity and morphology. The proposed approach has been applied to five public databases, namely INSPIRE, IOSTAR, VICAVR, DRIVE, and WIDE, and achieved high accuracies of 95.1%, 94.2%, 93.8%, 91.1%, and 91.0%, respectively. Furthermore, we have made manual annotations of the blood vessel topologies for INSPIRE, IOSTAR, VICAVR, and DRIVE datasets, and these annotations are released for public access so as to facilitate researchers in the community.

Original languageEnglish
Article number8754802
Pages (from-to)341-356
Number of pages16
JournalIEEE Transactions on Medical Imaging
Volume39
Issue number2
DOIs
Publication statusPublished - Feb 2020
Externally publishedYes

Keywords

  • Artery/vein classification
  • Retinal images
  • blood vessel
  • dominant set clustering
  • vascular topology

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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