根据图像灰度的联合概率分布函数与图像相似程度之间的变化规律,分析了Shannon互信息与K u llback-L e ib ler距离之间的关系,利用变量间的不等式关系理论,提出基于M inkow sk i不等式的广义距离度量,并构造了基于这一距离的多模态图像...根据图像灰度的联合概率分布函数与图像相似程度之间的变化规律,分析了Shannon互信息与K u llback-L e ib ler距离之间的关系,利用变量间的不等式关系理论,提出基于M inkow sk i不等式的广义距离度量,并构造了基于这一距离的多模态图像配准新测度.新的配准测度不再要求概率分布必须满足连续性的要求,实验中使用M R和PET医学图像进行了实验分析.结果显示,基于M inkow sk i距离的新配准测度比传统的信息论测度具有更强的噪声鲁棒性,用乘方运算代替了对数运算,数学表达式更简单,并省去了除法运算,在算法上也更容易实现.展开更多
A mutual information based 3D non-rigid registration approach was proposed for the registration of deformable CT/MR body abdomen images. The Parzen Windows Density Estimation (PWDE) method is adopted to calculate the ...A mutual information based 3D non-rigid registration approach was proposed for the registration of deformable CT/MR body abdomen images. The Parzen Windows Density Estimation (PWDE) method is adopted to calculate the mutual information between the two modals of CT and MRI abdomen images. By maximizing MI between the CT and MR volume images, the overlapping part of them reaches the biggest, which means that the two body images of CT and MR matches best to each other. Visible Human Project (VHP) Male abdomen CT and MRI Data are used as experimental data sets. The experimental results indicate that this approach of non-rigid 3D registration of CT/MR body abdominal images can be achieved effectively and automatically, without any prior processing procedures such as segmentation and feature extraction, but has a main drawback of very long computation time.展开更多
Mutual information has currently been one of the most intensivelyresearched measures. It has been proven to be accurate and effective registrationmeasure. Despite the general promising results, mutual information some...Mutual information has currently been one of the most intensivelyresearched measures. It has been proven to be accurate and effective registrationmeasure. Despite the general promising results, mutual information sometimes mightlead to misregistration because of neglecting spatial information and treating intensityvariations with undue sensitivity. In this paper, an extension of mutual informationframework was proposed in which higher-order spatial information regarding imagestructures was incorporated into the registration processing of PET and MR. Thesecond-order estimate of mutual information algorithm was applied to the registrationof seven patients. Evaluation from Vanderbilt University and our visual inspectionshowed that sub-voxel accuracy and robust results were achieved in all cases withsecond-order mutual information as the similarity measure and with Powell's multi-dimensional direction set method as optimization strategy.展开更多
文摘根据图像灰度的联合概率分布函数与图像相似程度之间的变化规律,分析了Shannon互信息与K u llback-L e ib ler距离之间的关系,利用变量间的不等式关系理论,提出基于M inkow sk i不等式的广义距离度量,并构造了基于这一距离的多模态图像配准新测度.新的配准测度不再要求概率分布必须满足连续性的要求,实验中使用M R和PET医学图像进行了实验分析.结果显示,基于M inkow sk i距离的新配准测度比传统的信息论测度具有更强的噪声鲁棒性,用乘方运算代替了对数运算,数学表达式更简单,并省去了除法运算,在算法上也更容易实现.
基金An international cooperation project between Shanghai Jiaotong U niversity and Hong Kong Polytechnic University
文摘A mutual information based 3D non-rigid registration approach was proposed for the registration of deformable CT/MR body abdomen images. The Parzen Windows Density Estimation (PWDE) method is adopted to calculate the mutual information between the two modals of CT and MRI abdomen images. By maximizing MI between the CT and MR volume images, the overlapping part of them reaches the biggest, which means that the two body images of CT and MR matches best to each other. Visible Human Project (VHP) Male abdomen CT and MRI Data are used as experimental data sets. The experimental results indicate that this approach of non-rigid 3D registration of CT/MR body abdominal images can be achieved effectively and automatically, without any prior processing procedures such as segmentation and feature extraction, but has a main drawback of very long computation time.
基金The images and the standard transformation were provided as part of the project,"Retrospective Im-age Registration Evaluation"(National Institutes of Health,1 R01 CA89323),the principal investigator,J.Michael Fitzpatrick,Vanderbilt Universi
文摘Mutual information has currently been one of the most intensivelyresearched measures. It has been proven to be accurate and effective registrationmeasure. Despite the general promising results, mutual information sometimes mightlead to misregistration because of neglecting spatial information and treating intensityvariations with undue sensitivity. In this paper, an extension of mutual informationframework was proposed in which higher-order spatial information regarding imagestructures was incorporated into the registration processing of PET and MR. Thesecond-order estimate of mutual information algorithm was applied to the registrationof seven patients. Evaluation from Vanderbilt University and our visual inspectionshowed that sub-voxel accuracy and robust results were achieved in all cases withsecond-order mutual information as the similarity measure and with Powell's multi-dimensional direction set method as optimization strategy.
