Journal of Digital Imaging

, Volume 31, Issue 6, pp 857–868 | Cite as

An Unsupervised Approach for Extraction of Blood Vessels from Fundus Images

  • Jyotiprava DashEmail author
  • Nilamani Bhoi


Pathological disorders may happen due to small changes in retinal blood vessels which may later turn into blindness. Hence, the accurate segmentation of blood vessels is becoming a challenging task for pathological analysis. This paper offers an unsupervised recursive method for extraction of blood vessels from ophthalmoscope images. First, a vessel-enhanced image is generated with the help of gamma correction and contrast-limited adaptive histogram equalization (CLAHE). Next, the vessels are extracted iteratively by applying an adaptive thresholding technique. At last, a final vessel segmented image is produced by applying a morphological cleaning operation. Evaluations are accompanied on the publicly available digital retinal images for vessel extraction (DRIVE) and Child Heart And Health Study in England (CHASE_DB1) databases using nine different measurements. The proposed method achieves average accuracies of 0.957 and 0.952 on DRIVE and CHASE_DB1 databases respectively.


Retinal blood vessels Ophthalmoscope CLAHE Gamma correction 


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Copyright information

© Society for Imaging Informatics in Medicine 2018

Authors and Affiliations

  1. 1.Department of Electronic & Tele-communication EngineeringVeer Surendra Sai University of TechnologyBurlaIndia

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