Nonrigid image registration using conditional mutual information.

We propose conditional mutual information (cMI) as a new similarity measure for nonrigid image registration. We start from a 3D joint histogram incorporating, besides the reference and floating intensity dimensions, also a spatial dimension expressing the location of the joint intensity pair in the reference image. cMI is calculated as the expectation value of the conditional mutual information between the reference and floating intensities given the spatial distribution. Validation experiments were performed comparing cMI and global MI on artificial CT/MR registrations and registrations complicated with a strong bias field; both a Parzen window and generalised partial volume kernel were used for histogram construction. In both experiments, cMI significantly outperforms global MI. Moreover, cMI is compared to global MI for the registration of three patient CT/MR datasets, using overlap and centroid distance as validation measure. The best results are obtained using cMI.