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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
Article

Abstract

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.

Keywords

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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