A new unsupervised algorithm for image segmentation based on an inhomogeneous Markov random field model / 南方医科大学学报
Journal of Southern Medical University
; (12): 1646-1648, 2007.
Article
in Zh
| WPRIM
| ID: wpr-281572
Responsible library:
WPRO
ABSTRACT
A new unsupervised algorithm for image segmentation is proposed using an inhomogeneous Markov random field (MRF) model, in which the parameter is estimated in fuzzy spel affinities. The proposed algorithm improved the accuracy of segmentation. Simulated brain MR image with different noise levels and clinical brain MR image were presented in the experiments. The results showed that the proposed algorithm was more powerful than conventional homogeneous MRF model-based ones and than the fuzzy c-means clustering algorithm as well.
Full text:
1
Index:
WPRIM
Main subject:
Algorithms
/
Brain
/
Magnetic Resonance Imaging
/
Image Interpretation, Computer-Assisted
/
Markov Chains
/
Fuzzy Logic
/
Methods
Type of study:
Clinical_trials
/
Health_economic_evaluation
/
Prognostic_studies
Limits:
Humans
Language:
Zh
Journal:
Journal of Southern Medical University
Year:
2007
Type:
Article