Abstract: With the development of image processing field, more and more effective image segmentation algorithms, in medical image processing, brain and blood vessel magnetic resonance image segmentation, lung nodules automatic detection and tumors detection have been recently proposed. This paper will consider the brain MRI segmentation problems. It is worthy of noting that although many traditional clustering algorithms of medical image segmentation like K-means, Fuzzy C-means (FCM) and general Gaussian mixture model can effectively deal with these medical images without noise, they cannot perform well in noisy medical images which often exist in original medical image processing. Therefore, it is quite necessary and actually required for researching the image segmentation algorithm with noise elimination. To this end, this paper propses an image segmentation method based on Gaussian mixture model with Dirichlet distribution and parameter analysis, which can effectively denoise the medical images. Firstly, Gaussian function is adopted to calculate pixels' prior probabilities. Dirichlet distribution and Dirichlet law are utilized to construct the local spatial information among pixels. And then, the gradient descent method is used to optimize the loss function and update model parameters at the same time. Finally, the probability of each pixel for each label can be obtained and the pixel's highest prediction probability in all labels can be selected as the clustering result. This paper performs four clustering segmentation algorithms on normal creatures and brain MR images with Gaussian noise and salt &pepper noise. The experiment results show that image segmentation on normal creatures with three and four labels have a good result to cluster pixels which especially belong to background. Also, the proposed algorithm used to segment brain MR images with Gaussian noise and salt &pepper noise is more precisely and clearer than other three traditional algorithms. In conclusion, the experiment results show that the proposed method is better than the traditional image segmentation methods which has the lowest segmentation error rate and fine definition.