高斯 马尔可夫随机场模型既利用了图像像素的灰度信息，又通过像素类别标记的Gibbs光滑先验概率引入了图像的空间信息，是能较好地分割含有噪声图像的模型，然而，Gibbs惩罚因子β的确定却一直是个难点，为获得好的分割效果，通常用多个β值人工尝试。本文针对此问题，提出了一种新的、简单的、类自适应的惩罚因子β，其利用后验概率来自动计算，并具有各类各向异性。再将模型利用EM MAP算法来迭代求解。最后，将该算法应用于MR图像的分割，实验结果表明，该算法能自适应地、有效地分割噪声图像，并具有较高的正确分类

Gauss Markov random field model takes advantage of both image intensity and spatial information imposed by Gibbs smoothness prior to the pixel labels and thus can be used to effectively in segmenting the noisy imagesHowever it is always difficult to confirm the Gibbs penalty factor βAs usual，to get good segmentation result for every segmenting to be image，various values of β will be tested by handSo to solve this problem，this article defines a new and simple class adaptive penalty factor βIt is automatically calculated from the posterior probability and is anisotropic for each classFurthermore the model iteratively obtains their parameters estimation in the EM MAP algorithmFinally，by application of this algorithm to brain MR Image segmentation，the proposed segmentation scheme is proved effective for noisy image and at the same time it distinguishes itself by higher correct classification ratio and correct classification ratio for each class