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Information Theoretic Deformable Registration Using Local Image Information

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Abstract

We present a deformable registration algorithm for multi-modality images based on information theoretic similarity measures at the scale of individual image voxels. We derive analytical expressions for the mutual information, the joint entropy, and the sum of marginal entropies of two images over a small neighborhood in terms of image gradients. Using these expressions, we formulate image registration algorithms maximizing local similarity over the whole image domain in an energy minimization framework. This strategy produces highly elastic image alignment as the registration is driven by voxel similarities between the images, the algorithms are easily implementable using the closed-form expressions for the derivative of the optimization function with respect to the deformation, and avoid estimation of joint and marginal probability densities governing the image intensities essential to conventional information theoretic image registration methods.

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Authors and Affiliations

  1. Center for Integrated Bioinformatics, School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, PA, 19104

    Bilge Karaçali

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  1. Bilge Karaçali
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Correspondence to Bilge Karaçali.

Additional information

This work has been supported in part by NIH grants R01-NS42645 and R01-AG14971.

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Karaçali, B. Information Theoretic Deformable Registration Using Local Image Information. Int J Comput Vision 72, 219–237 (2007). https://doi.org/10.1007/s11263-006-8704-0

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  • DOI: https://doi.org/10.1007/s11263-006-8704-0

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