Vector-Valued Local Frequency Representation for Robust Multimodal Image Registration

Automatic registration of multi-modal images involves algorithmically estimating the coordinate transformation required to align the data sets. Most existing methods in literature are unable to cope with registration of image pairs with large non-overlapping field of view (FOV). We propose a robust algorithm, based on matching vector-valued local frequency (corresponding to the dominant local frequency magnitude) image representations, which can cope with image pairs with large non-overlapping FOV. The work reported here is an extension of our earlier work which used dominant local frequency magnitude based representations of the input data as input to the matching algorithm. The local frequency representation naturally allows for processing the data at different scales/resolutions, a very desirable property from a computational efficiency view point. Our algorithm involves minimizing - over all affine transformations - the integral of the squared error (ISE or L₂E) between a Gaussian model of the residual and its true density function. The residual here refers to the difference between the local frequency representations of the transformed (by an unknown transformation) source and target data. We present implementation results for misalignments between MR brain scans obtained using different image acquisition protocols