Research on K-means clustering segmentation method for MRI brain image based on selecting multi-peaks in gray histogram / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 1164-1170, 2013.
Article
in Chinese
| WPRIM
| ID: wpr-259747
ABSTRACT
To solve the problem of traditional K-means clustering in which initial clustering centers are selected randomly, we proposed a new K-means segmentation algorithm based on robustly selecting 'peaks' standing for White Matter, Gray Matter and Cerebrospinal Fluid in multi-peaks gray histogram of MRI brain image. The new algorithm takes gray value of selected histogram 'peaks' as the initial K-means clustering center and can segment the MRI brain image into three parts of tissue more effectively, accurately, steadily and successfully. Massive experiments have proved that the proposed algorithm can overcome many shortcomings caused by traditional K-means clustering method such as low efficiency, veracity, robustness and time consuming. The histogram 'peak' selecting idea of the proposed segmentootion method is of more universal availability.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Brain
/
Magnetic Resonance Imaging
/
Cluster Analysis
/
Neuroimaging
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2013
Type:
Article
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