Deformable Registration of MR Brains with Best Features  

作　　者：吴国荣 戚飞虎 史勇红 栾红霞 

机构地区：[1]Dept. of Computer Science and Eng.,Shanghai Jiaotong Univ.,Shanghai 200030, China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2006年第3期290-295,共6页上海交通大学学报（英文版）

基　　金：National Natural Science Foundation of China(No.60271033)

摘　　要：A learning-based deformable registration method was presented for MR brain images. First, best geometric features are selected for each location and each resolution, in order to reduce ambiguity in matching during image registration. The best features are obtained by solving an energy minimization problem, which requires the features to be distinctive around the neighboring points and consistency across training samples. Secondly, the set of active points is hierarchically selected based on their saliency and consistency measurements during registration, which helps to produce accurate registration results. Finally, by incorporating those learned results into the framework of HAMMER, great improvement in both real data and simulated data is achieved.A learning-based deformable registration method was presented for MR brain images. First, best geometric features are selected for each location and each resolution, in order to reduce ambiguity in matching during image registration. The best features are obtained by solving an energy minimization problem, which requires the features to be distinctive around the neighboring points and consistency across training samples. Secondly, the set of active points is hierarchically selected based on their saliency and consistency measurements during registration, which helps to produce accurate registration results. Finally, by incorporating those learned results into the framework of HAMMER, great improvement in both real data and simulated data is achieved.

关 键 词：deformable registration machine learning best scale selection 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]