By Topic

Fuzzy clustering with spatial constraints

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

1 Author(s)
D. L. Pham ; Lab. of Personality & Cognition, Gerontology Res. Center, Baltimore, MD, USA

A novel approach to fuzzy clustering for image segmentation is described. The fuzzy C-means objective function is generalized to include a spatial penalty on the membership functions. The penalty term leads to an iterative algorithm that is only slightly different from the original fuzzy C-means algorithm and allows the estimation of spatially smooth membership functions. To determine the strength of the penalty term, a criterion based on cross-validation is employed. The new algorithm is applied to simulated and real magnetic resonance images and is shown to be more robust to noise and other artifacts than the standard algorithm.

Published in:

Image Processing. 2002. Proceedings. 2002 International Conference on  (Volume:2 )

Date of Conference: