Fuzzy Markov random filed model and a new algorithm for image segmentation / 南方医科大学学报
Journal of Southern Medical University
;
(12): 579-583, 2006.
Article
in Chinese
| WPRIM
| ID: wpr-255248
ABSTRACT
A fuzzy Markov random field (FMRF) model is established and a new algorithm based on FMRF for image segmentation proposed in this paper. This algorithm simultaneously deals with the fuzziness and randomness for effective acquisition of the prior knowledge of the images. A conventional Markov random field (CMRF) serves as a bridge between the FMRF, obviously a generalization of the CMRF, and the original images. The FMRF degenerates into the CMRF when no fuzziness is considered. The segmentation results are obtained by fuzzifying the image, updating the membership of prior FMRF based on the maximum posteriori criteria, and defuzzifying the image according to the maximum membership principle. The proposed algorithm can effectively filter the noise and eliminate partial volume effect when processing the degraded image to ensure more accurate image segmentation.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Signal Processing, Computer-Assisted
/
Pattern Recognition, Automated
/
Image Interpretation, Computer-Assisted
/
Image Enhancement
/
Markov Chains
/
Fuzzy Logic
/
Methods
Type of study:
Controlled clinical trial
/
Health economic evaluation
/
Prognostic study
Limits:
Humans
Language:
Chinese
Journal:
Journal of Southern Medical University
Year:
2006
Type:
Article
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