A new algorithm for magnetic resonance image segmentation based on fuzzy kerne1 clustering.
- Author:
Xue-fei YU
1
;
Bin LI
;
Wu-fan CHEN
Author Information
1. School of Biomedical Engineering, Southern Medical University, Guangzhou 510515ìChina. xuefeiyu@fimmu.com
- Publication Type:Journal Article
- MeSH:
Algorithms;
Cluster Analysis;
Fuzzy Logic;
Image Interpretation, Computer-Assisted;
methods;
Magnetic Resonance Imaging;
Pattern Recognition, Automated;
methods
- From:
Journal of Southern Medical University
2008;28(4):555-557
- CountryChina
- Language:Chinese
-
Abstract:
Fuzzy clustering technique is a popular model widely used in the segmentation of magnetic resonance (MR) images. However, when the conventional fuzzy clustering algorithm is used for image segmentation, the algorithm strictly depending on the current pixels works only on images with less noise. In the paper, we presented a modified fuzzy kernel clustering algorithm for MR image segmentation. The new algorithm incorporates a kernel-induced distance mertric and a penalty term that controls the neighborhood effect to the objective function. The results of experiment on both the synthetic images and simulated MR images show that the proposed algorithm is more robust to noise than the standard fuzzy image segmentation algorithms.
