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As magnetic resonance imaging (MRI) is an important technology of radiological evaluation and computer-aided diagnosis, the accuracy of the MR image segmentation directly influences the validity of following processing. In general, the Gaussian mixture model (GMM) is highly effective for MR image segmentation. But for the conventional GMM appling in image segmentation, cluster assignment is based solely on the distribution of pixel attributes in the feature space, and the spatial distribution of pixels in an image is not taken into consideration. In this paper, we present a novel GMM scheme by utilizing local contextual information and the high inter-pixel correlation inherent for the segmentation of brain MR image. Firstly, a local spatial function is established, and the class probabilities of very pixels according to bayesian rules are determined adaptively based on local spatial function. Secondly, Expectation Maximization algorithm as an optimization method is used to obtain iterative formula of E-step and M-step for the proposed model Finally, the segmentation experiments by synthetic image and real image demonstrate that the proposed method can get a better classification result.