By Topic

Retinal vessel segmentation using spatially weighted fuzzy c-means clustering and histogram matching

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Kande, G.B. ; ECE Dept., S.R.K. Inst. of Technol., Vijayawada ; Savithri, T.S. ; Subbaiah, P.

This paper presents a novel approach for automated segmentation of the vasculature in retinal images. The approach uses the intensity information from red and green channels of the same retinal image to correct nonuniform illumination in colour fundus images. Matched filtering is utilized to enhance the contrast of blood vessels against the background. The enhanced blood vessels are then segmented by employing spatially weighted fuzzy c-means clustering based thresholding which can well maintain the spatial structure of the vascular tree segments. Experimental results of the proposed method using STARE and DRIVE databases are superior to previously reported unsupervised methods and comparable to those obtained with the supervised methods.

Published in:

India Conference, 2008. INDICON 2008. Annual IEEE  (Volume:1 )

Date of Conference:

11-13 Dec. 2008