主动形体模型法在肝脏CT图像分割中的应用

主动形体模型(ASM模型)具有较强的针对柔性体建模的能力，在医学图像处理领域具有广泛的应用，与主动轮廓模型(Snake)相比，AsM模型运用了训练集的统计形状信息来进行分割，避免了训练集大量先验信息的浪费．首次将主动形体模型运用在真实的肝脏CT图像的分割中，建立起一个肝脏统计形体模型并运用在肝脏cT的分割中．实验中将ASM模型和Snake模型的分割结果进行比较，结果表明：运用主动形体模型进行肝脏CT图像的分割可以取到更好的匹配效果，并能大大缩短目标定位的时间．

Application of active shape model in segmentation of liver CT image

Active Shape Model(ASM) has strong modeling capability for flexible objects and it has a wild range of applications in the field of medical image processing.Comparing with the Active Contour Model(Snake),ASM model used the statistics shape information in training set for image segmentation.It will avoid wasting a lot of prior information of training set.We firstly applied Active Shape Model in the segmentation of the real liver CT images.We established a liver statistical shape model and applied it in the segmentation of liver CT images.In the experiment,the segmentation results of ASM model and Snake model are compared.The result shows that using Active Shape Model for the segmentation of liver CT images can gain a better match result,and the time of targeting will be shorten greatly.