t-mixture model clustering based on genetic K-means initialization for medical images
遗传K-均值初始化的t混合模型医学图像聚类*

The clustering quality of the mixture model clustering for images is vulnerable to the initial values of the mixture model parameters. To solve this problem, this paper proposed one method that based on initialization of genetic K-means algorithm of t mixture model for medical images. It built a t mixture model of medical image, and integrated genetic algorithm with K-means algorithm to realize the initial division of medical images, and then got the initial values of the mixture model. It could effectively overcome the sensitivity of mixture model to the initial selected parameter. Used EM algorithm to estimate the parameters of t mixture model. Finally, clustered the medical images at the base of the proposed mixture model. Experimental results show that medical images can be clustered accurately and the algorithm has great versatility and robustness.