Multimodal unbiased image matching via mutual information

In the past decade, information theory has been studied extensively in computational imaging. In particular, image matching by maximizing mutual information has been shown to yield good results in multimodal image registration. However, there have been few rigorous studies to date that investigate the statistical aspect of the resulting deformation fields. Different regularization techniques have been proposed, sometimes generating deformations very different from one another. In this paper, we present a novel model for multimodal image registration. The proposed method minimizes a purely information-theoretic functional consisting of mutual information matching and unbiased regularization. The unbiased regularization term measures the magnitude of deformations using either asymmetric Kullback-Leibler divergence or its symmetric version. The new multimodal unbiased matching method, which allows for large topology preserving deformations, was tested using pairs of two and three dimensional serial MRI images. We compared the results obtained using the proposed model to those computed with a well-known mutual information based viscous fluid registration. A thorough statistical analysis demonstrated the advantages of the proposed model over the multimodal fluid registration method when recovering deformation fields and corresponding Jacobian maps.