title: Nonrigid image registration using conditional mutual information.
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.
authors.affiliation: Group of Medical Image Computing, Center for Processing Speech and Images, Department of Electrical Engineering, Faculty of Engineering, Katholieke Universiteit Leuven, 3000 Leuven, Belgium. dirk.loeckx@uz.kuleuven.ac.be
authors.forename: Dirk
authors.initials: D
authors.lastname: Loeckx
authors.forename: Pieter
authors.initials: P
authors.lastname: Slagmolen
authors.forename: Frederik
authors.initials: F
authors.lastname: Maes
authors.forename: Dirk
authors.initials: D
authors.lastname: Vandermeulen
authors.forename: Paul
authors.initials: P
authors.lastname: Suetens
mesh_terms: D000465:Algorithms; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007257:Information Theory; D008279:Magnetic Resonance Imaging / Q000379:methods; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed / Q000379:methods