Abstract: In order to solve the problems of misidentification of brain tumor regions in MRI and the uncertainty in segmentation of tumor sites in brain MRI images， an improved Kmeans algorithm combined with hidden Markov random field （HMRF) model  is proposed to achieve accurate segmentation of brain tumor images. Firstly， the Euclidean distance of Kmeans algorithm is replaced by Manhattan-Chebyshev distance and the improved Kmeans algorithm is used to estimate the initial parameters and initial segmentation of the image to be segmented. Then the spatial information of the image is obtained by HMRF theory and the clustering center is updated by combining with EM algorithm to obtain more accurate clustering center so as to improve the segmentation performance of the algorithm. The experimental results show that the proposed method has good performance effect of brain tumor segmentation， in which the average values of Dice coefficient and Jaccard coefficient reach 0.9289 and 0.8725， respectively.

Key words: segmentation of brain tumor, Kmeans algorithm, HMRF, EM algorithm, clustering center