By Topic

Segmentation of Magnetic Resonance Images Using Discrete Curve Evolution and Fuzzy Clustering

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

4 Author(s)
Supot, S. ; King Mongkut ''s Inst. of Technol. Ladkrabang, Bangkok ; Thanapong, C. ; Chuchart, P. ; Manas, S.

The region clustering of a magnetic resonance imaging (MRI) image is more complicate than a computed topography (CT) image because a MRI image composes of three components such as Tl -weighted, T2-weighted, and proton density (PD) in each layer. However, the MRI images provide more detail than the CT images. Therefore, we propose a technique of the region clustering of MRI image by using fuzzy c-means (PCM). The fuzzy c-means algorithm is an iterative operation, that is very time-consuming and makes the algorithm impractical for using in image segmentation. To cope with this problem, the discrete curve evolution (DCE) technique is applied to find the actual cluster center to refine the initial value of the fuzzy c-means algorithm, which reduces the convergence time. In experimental results, the proposed technique provides the same segmentation accuracy as the fuzzy c-means technique. Moreover, this technique takes lower computational time comparing to the previous method.

Published in:

Integration Technology, 2007. ICIT '07. IEEE International Conference on

Date of Conference:

20-24 March 2007