Abstract：
: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.
journal_name
IEEE Trans Med Imagingjournal_title
IEEE transactions on medical imagingauthors
Loeckx D,Slagmolen P,Maes F,Vandermeulen D,Suetens Pdoi
10.1109/TMI.2009.2021843subject
Has Abstractpub_date
2010-01-01 00:00:00pages
19-29issue
1eissn
0278-0062issn
1558-254Xjournal_volume
29pub_type
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