Brain tissue segmentation method based on maximum between-cluster variance optimized by the difference search algorithm / 国际生物医学工程杂志
International Journal of Biomedical Engineering
;
(6): 409-413,440, 2019.
Article
in Chinese
| WPRIM
| ID: wpr-805284
ABSTRACT
Objective@#To study a maximum between-cluster variance based on differential search algorithm, and to select the multi-threshold for effectively segmentation of brain magnetic resonance images.@*Methods@#The brain extraction tool(BET) algorithm was used to remove the non-brain tissue part of the original magnetic resonance image. The best-fit with coalescing(BFC) algorithm was used to remove the intensity non-uniformity. The differential search algorithm was used to optimize the maximum between-cluster variance of the image to find the optimal threshold for multi-threshold segmentation of the magnetic resonance image. The method was validated using simulated magnetic resonance(MR) brain image data provided by BrainWeb.@*Results@#For MR images with different noise levels and intensity inhomogeneities, the proposed method was better than FSL, SPM and Brainsuite methods.@*Conclusions@#The maximum between-cluster variance based on differential search algorithm has better segmentation accuracy and robustness, especially for cerebrospinal fluid.
Full text:
Available
Index:
WPRIM (Western Pacific)
Type of study:
Prognostic study
Language:
Chinese
Journal:
International Journal of Biomedical Engineering
Year:
2019
Type:
Article
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