New information theoretic distance measures and algorithms for multimodality image registration

First, we present a Bayesian multimodality non-rigid image registration method. We prove that the displacement field which minimizes the Bayesian maximum a posteriori (MAP) objective also maximizes the true mutual information (with a small deformation penalty) as the number of pixels tends to infinity. The criterion imposes an upper bound on the number of permissible configurations of the displacement field.; Second, we propose a new registration method for multimodality images, which combines features and intensities of the images in registration. The "best feature" is achieved by finding the best projection onto a single feature image by maximizing the NMI between the two registered images (training sets) w.r.t. the projection weights. We show that using the new best "feature" is more noise resistant than using image intensity as the default feature.; Finally, we present an extensible information metric for multimodality image registration. And its normalized version is still a pseudometric and is equivalent to normalized mutual information in the intermodality case. When compared to mutual information, it is easier to extend our metric to the registration of multiple images. After using a new technique to efficiently compute high dimensional histograms, the metric can be efficiently computed even in the multiple image case. And we use the metric and high dimensional histogram to affine and nonrigid multimodality image registration. In nonrigid registration, displacement field is represented by B-Spline and the multiresolution optimization algorithms are used for nonrigid intermodality and multimodality image registration. We compare the results of direct multimodality registration using high-dimensional histogramming with repeated intermodality registration. We find that registering 3 images simultaneously with the new metric is more accurate than pair-wise registration on the images obtained from synthetic magnetic resonance (MR) proton density (PD), MR T2 and MR T1 3D volumes from Brain Web. We perform the unbiased affine registration of 5 multimodality images of anatomy, CT, MR PD, T1 and T2 from Visible Human Male Data and the unbiased nonrigid registration of three MR 3D images of the brain with the normalized metric and high-dimensional histogramming. Our results demonstrate the efficacy of the metrics and high-dimensional histogramming in affine and nonrigid multimodality image registration.