A new algorithm for magnetic resonance image segmentation based on fuzzy kerne1 clustering / 南方医科大学学报
Journal of Southern Medical University
;
(12): 555-557, 2008.
Article
in Chinese
| WPRIM
| ID: wpr-280150
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.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Pattern Recognition, Automated
/
Magnetic Resonance Imaging
/
Image Interpretation, Computer-Assisted
/
Cluster Analysis
/
Fuzzy Logic
/
Methods
Type of study:
Prognostic study
Language:
Chinese
Journal:
Journal of Southern Medical University
Year:
2008
Type:
Article
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