Blood vessel segmentation of fundus images by major vessel extraction and subimage classification

Sohini Roychowdhury, Dara Koozekanani, Keshab K Parhi

Research output: Contribution to journalArticlepeer-review

158 Scopus citations

Abstract

This paper presents a novel three-stage blood vessel segmentation algorithm using fundus photographs. In the first stage, the green plane of a fundus image is preprocessed to extract a binary image after high-pass filtering, and another binary image from the morphologically reconstructed enhanced image for the vessel regions. Next, the regions common to both the binary images are extracted as the major vessels. In the second stage, all remaining pixels in the two binary images are classified using a Gaussian mixture model (GMM) classifier using a set of eight features that are extracted based on pixel neighborhood and first and second-order gradient images. In the third postprocessing stage, the major portions of the blood vessels are combined with the classified vessel pixels. The proposed algorithm is less dependent on training data, requires less segmentation time and achieves consistent vessel segmentation accuracy on normal images as well as images with pathology when compared to existing supervised segmentation methods. The proposed algorithm achieves a vessel segmentation accuracy of 95.2%, 95.15%, and 95.3% in an average of 3.1, 6.7, and 11.7 s on three public datasets DRIVE, STARE, and CHASE-DB1, respectively.

Original languageEnglish (US)
Article number6848752
Pages (from-to)1118-1128
Number of pages11
JournalIEEE Journal of Biomedical and Health Informatics
Volume19
Issue number3
DOIs
StatePublished - May 1 2015

Keywords

  • Classification
  • feature selection
  • fundus images
  • high-pass filter
  • morphological reconstruction
  • peripapillary vessel
  • vessel segmentation

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