Retinal vessels segmentation based on level set and region growing

Yu Qian Zhao, Xiao Hong Wang, Xiao Fang Wang, Frank Y. Shih

Research output: Contribution to journalArticlepeer-review

222 Scopus citations


Retinal vessels play an important role in the diagnostic procedure of retinopathy. Accurate segmentation of retinal vessels is crucial for pathological analysis. In this paper, we propose a new retinal vessel segmentation method based on level set and region growing. Firstly, a retinal vessel image is preprocessed by the contrast-limited adaptive histogram equalization and a 2D Gabor wavelet to enhance the vessels. Then, an anisotropic diffusion filter is used to smooth the image and preserve vessel boundaries. Finally, the region growing method and a region-based active contour model with level set implementation are applied to extract retinal vessels, and their results are combined to achieve the final segmentation. Comparisons are conducted on the publicly available DRIVE and STARE databases using three different measurements. Experimental results show that the proposed method reaches an average accuracy of 94.77% on the DRIVE database and 95.09% on the STARE database.

Original languageEnglish (US)
Pages (from-to)2437-2446
Number of pages10
JournalPattern Recognition
Issue number7
StatePublished - Jul 2014

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


  • 2D Gabor wavelet
  • Level set
  • Region growing
  • Retinal vessel segmentation

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