Structure-based level set method for automatic retinal vasculature segmentation

Bekir Dizdaroğlu, Esra Ataer-Cansizoglu, Jayashree Kalpathy-Cramer, Katie Keck, Michael F. Chiang, Deniz Erdogmus

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


Segmentation of vasculature in retinal fundus image by level set methods employing classical edge detection methodologies is a tedious task. In this study, a revised level set-based retinal vasculature segmentation approach is proposed. During preprocessing, intensity inhomogeneity on the green channel of input image is corrected by utilizing all image channels, generating more efficient results compared to methods utilizing only one (green) channel. A structure-based level set method employing a modified phase map is introduced to obtain accurate skeletonization and segmentation of the retinal vasculature. The seed points around vessels are selected and the level sets are initialized automatically. Furthermore, the proposed method introduces an improved zero-level contour regularization term which is more appropriate than the ones introduced by other methods for vasculature structures. We conducted the experiments on our own dataset, as well as two publicly available datasets. The results show that the proposed method segments retinal vessels accurately and its performance is comparable to state-of-the-art supervised/unsupervised segmentation techniques.

Original languageEnglish (US)
Article number39
JournalEurasip Journal on Image and Video Processing
Issue number1
StatePublished - Aug 11 2014
Externally publishedYes


  • Color retinal fundus images
  • Phase map
  • Segmentation of retinal vasculature
  • Structure and texture parts of retinal fundus image
  • Structure-based level set method

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Electrical and Electronic Engineering


Dive into the research topics of 'Structure-based level set method for automatic retinal vasculature segmentation'. Together they form a unique fingerprint.

Cite this