Fuzzy clustering image segmentation algorithms based on Gaussian mixture model is sensitive to noises and outliers. Therefor, it combines a prior probability with neighborhood relationship and Student’s-T distribution to construct a mixture model with spatial constrain. And then the object function of fuzzy clustering algorithm is defined by spatially constrained student’s-T mixture model and entropy regularization term. The characteristic of heavy-tails in Student’s-T distribution can overcome noise well than Gaussian distribution. In addition, in order to be more effective in sliding noise, a prior probability is constructed on the label field based on the interactions of pixel and its neighbors by Markov Random Filed, and expressed as the weight degree in mixture model to enhance robustness. The qualitative and quantitative analysis of the segmentation results for simulated image and real color images prove that the proposed algorithm is validity and feasibility.