A Semi-supervised Approach to Segment Retinal Blood Vessels in Color Fundus Photographs

  • Md. Abu Sayed
  • Sajib SahaEmail author
  • G. M. Atiqur Rahaman
  • Tanmai K. Ghosh
  • Yogesan Kanagasingam
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11526)


Segmentation of retinal blood vessels is an important diagnostic procedure in ophthalmology. In this paper we propose an automated blood vessels segmentation method that combines both supervised and un-supervised approaches. A novel descriptor named Local Haar Pattern (LHP) is proposed to describe retinal pixel of interest. The performance of the proposed method has been evaluated on three publicly available DRIVE, STARE and CHASE_DB1 datasets. The proposed method achieves an overall segmentation accuracy of 96%, 96% and 95% respectively on DRIVE, STARE, and CHASE DB1 datasets, which are better than the state-of-the-art methods.


Color fundus photographs Vessel segmentation Haar feature Multiscale line detector Random forest 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Md. Abu Sayed
    • 1
  • Sajib Saha
    • 2
    Email author
  • G. M. Atiqur Rahaman
    • 1
  • Tanmai K. Ghosh
    • 1
  • Yogesan Kanagasingam
    • 2
  1. 1.Computational Color and Spectral Image Analysis Lab, Computer Science and Engineering DisciplineKhulna UniversityKhulnaBangladesh
  2. 2.Australian e-Health Research Centre, CSIROFloreatAustralia

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