脑梗死病灶分割是评估脑功能损伤程度的重要预处理步骤。针对弥散加权成像(DWI) 图像中，脑梗死病灶边界模糊、形状不规则和亮度不均匀等特点，文中提出一种模糊速度函数驱动下活动轮廓模型的分割方法。首先，在活动轮廓模型轮廓初始化方面，利用小波变换域下的贝叶斯概率获取初始轮廓，该初始轮廓可快速定位于脑梗死病灶的真实边界附近，增强模型的鲁棒性和准确性。其次，将图像局部熵引入活动轮廓模型中。图像局部熵可表征脑梗死 DWI 图像水分子分布的差异性，在一定程度上解决图像亮度不均匀性和噪声的干扰问题。然后，根据脑梗死病灶的边界模糊特性，提出结合图像局部熵和灰度的模糊聚类算法计算模糊隶属度，进一步加强脑梗死病灶与正常组织的区分。最后，将基于模糊隶属度的模糊速度函数引入活动轮廓模型，构建能量泛函，使轮廓曲线在脑梗死病灶的模糊边界处停止演变，完成脑梗死病灶的分割。实验结果表明，文中提出的模型可以有效分割脑梗死病灶。

Segmentation of cerebral infarction focus is an important pre-processing step to assess functional injury of brain． To solve the low accuracy segmentation problem caused by blurred boundaries，irregular shapes and in-homogeneous intensities of cerebral infarctionin the image of diffusion weighted imaging (DWI)，a segmentation method of active contour model based on fuzzy speed function was proposed． Firstly，the Bayesian probability in the wavelet transform domain was used to obtain the initial contour，according to which the real boundary of cere-bral infarction focus can be quickly located，which may improve the segmentation accuracy of the model． Second-ly，the local entropy of the image was introduced into the active contour model． The local entropy of the image can represent water molecule distribution differences in DWI image，thud solve the problems of inhomogeneous intensities and noise interference to a certain extent． Thirdly，according to the fuzzy characteristics of the cerebral infarction fo-cus，a fuzzy clustering algorithm was proposed to calculate the fuzzy membership degree in the fuzzy speed function by combining the local entropy characteristics and gray-scale characteristics of the image，so as to further strengthen
the distinction between the cerebral infarction focus and the normal tissue． Finally，the fuzzy speed function was incor-porated into the active contour model to construct the energy function． The evolution of the contour curve was stopped at the fuzzy boundary of cerebral infarction focus，so that the segmentation of icerebral infarction was realized． The experi-ment results show that the proposed model can achieve accurate segmentation of icerebral infarction focus．