By Topic

Image segmentation using kernel fuzzy c-means clustering on level set method on noisy images

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
$33 $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

4 Author(s)
G. Raghotham Reddy ; Dept. of ECE, KITS, Warangal-15, India ; K. Ramudu ; Syed Zaheeruddin ; R. Rameshwar Rao

In this paper, kernel fuzzy c-means (KFCM) was used to generate an initial contour curve which overcomes leaking at the boundary during the curve propagation. Firstly, KFCM algorithm computes the fuzzy membership values for each pixel. On the basis of KFCM the edge indicator function was redefined. Using the edge indicator function the segmentation of medical images which are added with salt and pepper noise was performed to extract the regions of interest for further processing. The results of the above process of segmentation showed a considerable improvement in the evolution of the level set function.

Published in:

Communications and Signal Processing (ICCSP), 2011 International Conference on

Date of Conference:

10-12 Feb. 2011