A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection

Jia Zhang, Huiqi Li, Qing Nie, Li Cheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)

Abstract

A retinal vessel tracking method based on Bayesian theory and multi-scale line detection is proposed in this paper. The optic disk is located by a PCA method and the initial points of tracking are identified. In each step, candidate points for vessel edges are selected on a semi-ellipse. Three types of vessel structure are considered in the tracking: normal vessel, branching, and crossing. To determine the new pair of edge points, the characteristics of the vessel intensity profiles along both the cross section and the longitudinal direction are considered in the tracking. A Gaussian model is assumed in the cross section and multi-scale line detection is employed in the longitudinal direction. The advantage of the proposed method is that two dimensional vessel information is employed, which makes it work better than methods using one dimensional information only. Our method is tested on the REVIEW database and a comparison study is performed. Experimental results show that the proposed method is precise and robust in tracking vessel edges.
Original languageEnglish
Pages (from-to)517-525
Number of pages9
JournalComputerized Medical Imaging and Graphics
Volume38
Issue number6
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

Keywords

  • Bayesian theory
  • Multi-scale line detection
  • Retinal image
  • Vessel tracking

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Health Informatics
  • Radiological and Ultrasound Technology
  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition
  • Medicine(all)

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