%0 Journal Article
%T Multi-Modality Medical Image Registration Basedon Maximization of Mutual Information<br>基于最大互信息的多模医学图象配准
%A LUO Shu-qian
%A LI Xiang
%A <br>罗述谦
%A 李 响
%J 中国图象图形学报
%D 2000
%I 
%X In this paper a maximization of mutual information based multi -modality medical image registration method is described. The method presented in this paper applies mutual information to measure the information redundancy b etween the intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. MI is used as a measure o f similarity of two images. There exist many important technical issues to be so lved about the method such as how to compute MI more accurately and how to obtai n the maximization of MI, which are seldom mentioned in published papers. In thi s paper we provide some implementation issues, for example, subsampling, PV inte rpolation, outlier strategy. Powell searching algorithm is used which does not c ompute gradients. The combination of these computation techniques and searching strategy leads to a fast and accurate multi-modality image registration. The re gistration results of 3D human brain volume data of 41 CT-MR and 35 PET-MR fro m seven patients are validated to be subvoxel. The registration method is promis ing in clinical use.
%K Medical image
%K Registration
%K Mutual information
%K Rigid body tr ansformation
%K Multi-Modality<br>医学图象
%K 配准
%K 互信息
%K 刚体变换
%K 多模
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=709F3E165E42ACBE&yid=9806D0D4EAA9BED3&vid=94C357A881DFC066&iid=DF92D298D3FF1E6E&sid=CEFF71AEB051114C&eid=C7A2B92569DF5458&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=32&reference_num=8