关键词: 人脑MRI, 空间信息, 隐高斯混合模型, 牛顿迭代法, 期望最大化（EM）算法

Abstract: For the disadvantage of anti noise performance and robustness of traditional Gaussian mixture model, this paper presents a brain MR images segmentation method based on the hidden Gaussian mixture model. Due to neglect of the spatial information and the segmentation results distribution, the traditional Gaussian mixture model is imcomplete. In response to these shortcomings, in this paper, the probability density function of the segmentation results which is regarded as the hidden data is introduced into the Gaussian mixture model and a nonlinear weighted hidden Gaussian mixture model is established. Meanwhile, the Gaussian weighted exponent which contains spatial information and the smoothing factor is introduced. And Expectation-Maximization（EM） algorithm and Newton iteration method are used to calculate the class mean and variance, and the smoothing factor. Finally, the segmentation results are obtained according to the maximum a posteriori criterion. Experimental results show that the method proposed in this paper has good robustness and anti-noise performance to the human brain MRI.

Key words: human brain MRI, spatial information, Hidden Gaussian Mixture Model, Newton iteration method, Expectation-Maximization（EM） algorithm