Abstract：Confront with fuzzy C- means clustering is only suitable for the single feature data set and sensitive to noise,through the Markov random field is combing with feature selection Gauss mixture model,a random feature selection fuzzy clustering algorithm based on Markov is proposed.In the base of feature selection Gauss mixture model clustering objective function,the prior information of all the pixels in the neighborhood corresponding to the clustering pixel is utilized and combined with the theory of Markov random field,determining the prior probability of pixel classification.The KL divergence is used as the scale parameter to introduce the clustering function of the Gauss mixture model.The expression method of optimization is used to obtain the iterative solution of membership degree and cluster center etc,and then gives the corresponding algorithm for image segmentation.Through the segmentation of noise interference standard gray image and brain CT image,the test results show that the proposed algorithm is effective and has good robustness.
2018, Vol. 39 
