Current Issue Cover

冯 筠1,2, 叶豪盛2, 郭 竞1(1.西北大学信息技术学院, 西安 710027;2.香港城市大学计算机科学系 香港)

摘 要
An Abdominal Image Segmentation Algorithm based on Multi-resolution Statistical Model and Surface Recovery

FENG Jun1,2, YE Haosheng2, GUO Jing1(1.School of Information and Technology, Northwest University, Xian 710027;2.Department of Computer Science, City University of Hong Kong, Hong Kong, China)

The segmentation of abdominal CT series is a challenging task due to problems such as blur edges, large variance among individuals and small sample sizes. In this paper, a hybrid 3D surface segmentation algorithm based on a multi-resolution integrated model and missing data recovery technique is proposed. The appearance models to characterize the texture features around surface points are established, and the"confidence level (CFL)"for each point is defined. For the points which have high confidence, segmentation is accomplished by active image searching and model deformation. While for the points which have low confidence, instead of using unreliable edge information, data recovery technique is applied based on a statistical deformable model and available high confidence points. The experimental results demonstrate that the Hybrid-MISTO achieves the lowest segmentation error compared with a variety of state-of-the-art techniques such as Snake, ASM, and MISTO.