Soft Subspace Clustering Algorithm Optimized by Brain Storm Algorithm for Breast MR Image

Abstract

The traditional soft subspace clustering algorithm is very susceptible to the initial clustering center and noise data when segmenting breast MR images with large amount of information, uneven intensity and boundary blur, which results in that algorithm falls into local optimum and causes serious misclassification. Aiming at solving this problem, a soft subspace clustering algorithm improved by brain storm algorithm for breast MR images cluster-ing is proposed in this paper. Firstly, a new objective function combines relaxation criterion and generalized noise clustering, and the membership degree calculation method is used to find the subspace where the cluster class is located. Then, the clustering task in the subspace is adapted with a given index. Finally, the brain storm algorithm is used in the clustering process to balance local search and global search and overcomes the disadvantages that the existing algorithms are easy to fall into local optimum. The experimental results of the comparison algorithms and the proposed algorithm in Berkeley image dataset show that the proposed algorithm has higher precision, and the clustering results of clinical breast MR images verify the strong robustness of the proposed algorithm.

Keywords

• breast mr image
• brain storm algorithm
• soft subspace clustering algorithm
• image clustering