%0 Journal Article
%T Entropic graph estimation integrated with SIFT features for medical image non-rigid registration<br>融合SIFT特征的熵图估计医学图像非刚性配准
%A Zhang Shaomin
%A Zhi Liji
%A Zhao Dazhe
%A Lin Shukuan
%A Zhao Hong
%A <br>张少敏
%A 支力佳
%A 赵大哲
%A 林树宽
%A 赵宏
%J 中国图象图形学报
%D 2012
%I 
%X Accuracy is important for the regrstration of medical images.Pixel gray values are a widely used feature in image registration.However,the gray values come from a single source and ignore the spatial information.In some cases,it will cause misalignment.To solve the problem,entropic graph estimation integrated with SIFT features is proposed as a medical image non-rigid registration algorithm.In the algorithm,mutual information based rigid registration is used to roughly register two images.Then the pixel gray value and the SIFT features are extracted to form a k-nearest neighbor graph(kNNG),which is used to estimate α-mutual information(αMI).Comparison results of the images obtained from lung CT images and brain MRI images showed that the proposed algorithm provides better accuracy than both,the conventional rigid registration algorithm based on mutual information and the non-rigid registration algorithm based on entropic graph estimation and single pixel gray values.
%K 医学图像配准
%K SIFT描述子
%K k-最邻近图
%K α互信息
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=D06194629680C940ACE75262F54B9D85&aid=0EAA6E0F141A5D8E69A944F8597CEB69&yid=99E9153A83D4CB11&vid=BCA2697F357F2001&iid=38B194292C032A66&sid=216EFB25F7F834CC&eid=6B3068A7C27BD349&journal_id=1006-8961&journal_name=中国图象图形学报&referenced_num=0&reference_num=16