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

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.