Structure-based level set method for automatic retinal vasculature segmentation

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DİZDAROĞLU B., Ataer-Cansizoglu E., Kalpathy-Cramer J., KECK K., CHIANG M. F., Erdogmus D.

EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING, 2014 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume:
  • Publication Date: 2014
  • Doi Number: 10.1186/1687-5281-2014-39
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Keywords: Color retinal fundus images, Phase map, Segmentation of retinal vasculature, Structure and texture parts of retinal fundus image, Structure-based level set method, BLOOD-VESSEL SEGMENTATION, IMAGE SEGMENTATION, PLUS DISEASE, GRAY-LEVEL, RETINOPATHY, AGREEMENT, EVOLUTION, MODEL
  • Karadeniz Technical University Affiliated: Yes


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.