Non-Rigid Multimodal Medical Image Registration Based on the Conditional Statistics of the Joint Intensity Distribution

In this paper, we present a new methodology for multimodal non-rigid medical image registration. The proposed approach is based on combining a rigid registration achieved by a global optimization method, and a multimodal optical flow technique based on the conditional statistics of the joint intensity distribution (CS-JID) of the images to register. The methodology is essentially composed of two steps: first, the global deformation is approximated by using a rigid registration based on particle filtering; second, the optical flow is applied iteratively between the target and sequentially registered source image, by optimizing a new energy function that penalizes the difference between the intensities in one image with respect to the mean of the conditional intensity distribution of the other image, weighted by the conditional variance. After these steps, the non-rigid registration is made up by adding the resulting vector fields, computed by the rigid registration, and the sequential optical flow. The proposed algorithm was tested with three pairs of computed tomography (CT) and magnetic resonance (MR) images, aligned in the acquisition process, and subsequently warped with a synthetic non-rigid deformation. Preliminary results show that the methodology presents a good alternative for non-rigid multimodal registration, obtaining an average error of less than one pixel in the estimation of the deformation vector field in the majority of the cases.