Robust Multimodal Image Registration Using Local Frequency Representations

Abstract

Fusing of multi-modal data involves automatically estimating the coordinate transformation required to align the data sets. Most existing methods in literature are not robust and fast enough for practical use. We propose a robust algorithm, based on matching local-frequency image representations, which naturally allow 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 integral of the squared error (ISE or L 2E) 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. The primary advantage of our algorithm is its ability to cope with large non-overlapping fields of view of the two data sets being registered, a common occurrence in practise. We present implementation results for misalignments between CT and MR brain scans.KeywordsMutual InformationImage RegistrationLocal FrequencyLocal PhaseIntegral Square ErrorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.