基于模糊Gibbs随机场聚类二维直方图的核磁共振图像分割

针对人脑组织结构的不确定性和模糊性，提出模糊Gibbs随机场聚类与二维直方图相结合的分割方法。该方法首先利用均值、方差及邻域属性对隶属度函数进行定义，并建立模糊Gibbs随机场；然后以模糊Gibbs随机场作为先验知识、最大后验概率为判别准则来确定每一个像素的类归属以及它属于该类的隶属度，同时用模糊类的质心来更新类中心；最后将类中心引入二维直方图方法中，找到每个类之间的各个阈值点对图像进行分割。通过实验表明该算法能够准确分割出各种脑组织，对噪声的鲁棒性、结果的准确性及平滑性相对于模糊C均值(FCM)算法都有了很大的提高。

Clustering based on fuzzy Gibbs random field and2D histogram algorithm for MR image segmentation

For the uncertainty and the fuzziness of the organizational structure of human brain, an image segmentation algorithm that combines the clustering based on fuzzy Gibbs random field and the two-dimensional histogram method was proposed. In the algorithm, membership functions were defined by average, variance and neighborhood attributes, and fuzzy Gibbs random field was set up. Then Maximum A Posteriori (MAP) was used as the statistical clustering criteria, in which the fuzzy Gibbs random field was used to obtain prior knowledge, and every class center was updated by the centroid of the fuzzy class. Finally, every class center was introduced into two-dimensional histogram method to find segmentation points in each class region for image segmentation. The experimental results show that the proposed algorithm can separate out the various brain tissues accurately, and it is better than Fuzzy C-Means (FCM) algorithm in the noise robustness, the result accuracy and smoothness.