基于t-混合模型的脑MR图像白质分割

核磁共振成像（MRI）作为临床辅助诊断和研究的重要工具，MR图像分割的准确性直接影响着后续处理的正确性和有效性。在目前的图像分割算法中，基于t-混合模型的图像分割方法因其快速和稳健性而受到重视。该方法的一般过程是先估计混合模型的参数，计算图像中每点的后验概率，然后根据贝叶斯最小错误率准则对图像进行分割。根据MR图像的特点，提出了基于t-混合模型的大脑MR图像白质分割的算法，并取得了较好的实验结果。

White matter segmentation of brain MR images based on t-mixture model

As Magnetic Resonance Imaging（MRI） is an important technology of clinical diagnosis and research,the accuracy of the MR image segmentation directly influences the validity of following processing.The segmentation method based on t-mixture model receives attention because of its speediness and robustness among current methods.The main procedure is outlined as follows.Firstly,the parameters of t-mixture model are estimated,and then the posterior probability of the pixels of the image is computed.At last the image is segmented according to Bayes decision rule for minimum error.By analyzing the features of MR images,the algorithm of white matter segmentation of brain MR images based on t-mixture model is proposed,and better experimental results are obtained.