Multimodal image registration using local frequency

Fusing of multi-modal data involves automatically estimating the coordinate transformation required to align the multi-modal image data sets. Most existing methods in literature are not fast enough (take hours for estimating nonrigid deformations) for practical use. We propose a very fast algorithm, based on matching local-frequency image representations, which naturally allows for processing the data at different scales/resolutions, a very desirable property from a computational efficiency view point. This algorithm involves minimizing-over all affine transformations-the expectation of the squared difference between the local-frequency representations of the source and target images. In cases where fusing the multi-modal data requires estimating the non-rigid deformations, we propose a novel and fast PDE-based morphing technique that will estimate this non-rigid alignment. We present implementation results for synthesized and real misalignments between CT and MR brain scans. In both the cases, we validate our results against ground truth registrations which for the former case are known and for the latter are obtained from manual registration performed by an expert