Liver Segmentation Using a Statistical Shape Model Based on Learning Local Objective Functions

摘要：

　　This Model-based segmentation approach plays an important role in organ segmentation from 3D CT volumes,but the accuracy is not enough of organs which are of the large variations in the shapes and the intensity pattern inside and along the boundaries.However,the methods based statistical shape models (SSMs) have a better performance among the approaches.But the automatic liver segmentation is still a challenging work.In this paper,a novelty method is proposed,which is based on statistical shape model integrated a new intensity model,combining the edge information with region intensity information for liver segmentation.First,edge information is extracted from the distance image obtained by distance translation.Second,we analyze region intensity information and construct a weighted Gaussian mixture model.Then an objective function is learned from the training data to fit the ideal objective function,which dynamically adjust the weights of the different features in image segmentation procedure.The experiment results evaluated on the test data,demonstrate the approach not only produces excellent segmentation accuracy,but also increases the robustness.

关键词： SSM intensity model edge feature region feature learned objective function

作者: Chunli Li Jiulou Zhang Qianjin Feng Wufan Chen

作者单位: School of Biomedical Engineering, Southern Medical University, Guangzhou, China

会议类型: 国际会议

会议名称: World Congress on Medical Physics and Biomedical Engineering (2012年医学物理及生物医学工程国际会议(IFMBE))

会议地点: 北京

会议语种:英文

页码: 892-895

在线出版日期: 2012-05-26（万方平台首次上网日期，不代表论文的发表时间）