提出了一种采用活动形状模型的图像自动分割方法，用于对放疗中CT前列腺图像的自动分割。活动形状模型的关键问题包括形状模型与表面模型的构建，本文利用尺度不变局部特征在前列腺图像边界上的特异性，建立了精确的前列腺表面模型。为了更好地描述特定病人前列腺形状变化，本文提出了在线学习训练机制，在当前病人样本数较少的情况下，采用群体统计信息建立形状模型，随当前病人样本数的增加，逐步增加当前病人样本统计信息在对构建形状模型的权重。本文对24个病人的共264套图像进行了实验，结果显示平均Dice相似性系数为90.5%，平均表面距离为1.90mm，表明本文方法有很高精确，264套图像中只有一套图像的Dice相似性系数小于70%，表明本方法有很好的鲁棒性。

This paper presents a new active shape models(ASMs) based method to segment the prostate from CT images for the radiotherapy. The key point of ASMs is the construction of both shape model and appearance model. We utilize the scale invariant feature transform(SIFT) local descriptor， which is more distinctive than general intensity and gradient features on the edges of the prostate boundary in the CT images， to characterize the image features and build the appearance model. To accurately capture prostate shape variation， an online training mechanism is proposed to build the shape model. When the samples of current patient are limited， the population statistics is used to build the shape model. As the increase of the samples of current patient， the patient-specific statistics plays an important role for constructing the shape model gradually. We test our method on a data set including 264 images of 24 patients， the average Dice similarity coefficient (DSC) is 90.5% and the mean average surface distance(ASD) is 1.90mm. The results show that the proposed method is robust and accurate.