Nonrigid Image Registration Using Conditional Mutual Information
Nonrigid Image Registration Using Conditional Mutual Information
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DOI：
10.1109/tmi.2009.2021843
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发表时间：
2010-01-01
影响因子：
10.6
通讯作者：
Suetens, Paul
中科院分区：
文献类型：
--
作者：
Loeckx, Dirk;Slagmolen, Pieter;Suetens, Paul
互信息最大化（MMI）是医学图像配准中常用的相似性度量。虽然它的准确性和鲁棒性已被证明为刚体图像配准，扩展MMI非刚性图像配准是不平凡的，一个活跃的研究领域。我们提出了条件互信息（cMI）作为一种新的相似性度量的非刚性图像配准。cMI从3-D联合直方图开始，除了强度维度之外，还包括表示联合强度对的位置的空间维度。cMI被计算为给定空间分布的图像强度之间的cMI的期望值。cMI措施被纳入张量积B样条非刚性配准方法，使用Parzen窗口或广义部分体积核直方图建设。在理论、体模和临床环境中，将cMI与经典的全局互信息（gMI）方法进行比较。我们表明，cMI显着优于gMI的所有应用程序。
Maximization of mutual information (MMI) is a popular similarity measure for medical image registration. Although its accuracy and robustness has been demonstrated for rigid body image registration, extending MMI to nonrigid image registration is not trivial and an active field of research. We propose conditional mutual information (cMI) as a new similarity measure for nonrigid image registration. cMI starts from a 3-D joint histogram incorporating, besides the intensity dimensions, also a spatial dimension expressing the location of the joint intensity pair. cMI is calculated as the expected value of the cMI between the image intensities given the spatial distribution. The cMI measure was incorporated in a tensor-product B-spline nonrigid registration method, using either a Parzen window or generalized partial volume kernel for histogram construction. cMI was compared to the classical global mutual information (gMI) approach in theoretical, phantom, and clinical settings. We show that cMI significantly outperforms gMI for all applications.