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Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy

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Abstract

In this paper we present a new approach for the non-rigid registration of multi-modality images. Our approach is based on an information theoretic measure called the cumulative residual entropy (CRE), which is a measure of entropy defined using cumulative distributions. Cross-CRE between two images to be registered is defined and maximized over the space of smooth and unknown non-rigid transformations. For efficient and robust computation of the non-rigid deformations, a tri-cubic B-spline based representation of the deformation function is used. The key strengths of combining CCRE with the tri-cubic B-spline representation in addressing the non-rigid registration problem are that, not only do we achieve the robustness due to the nature of the CCRE measure, we also achieve computational efficiency in estimating the non-rigid registration. The salient features of our algorithm are: (i) it accommodates images to be registered of varying contrast+brightness, (ii) faster convergence speed compared to other information theory-based measures used for non-rigid registration in literature, (iii) analytic computation of the gradient of CCRE with respect to the non-rigid registration parameters to achieve efficient and accurate registration, (iv) it is well suited for situations where the source and the target images have field of views with large non-overlapping regions. We demonstrate these strengths via experiments on synthesized and real image data.

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Authors and Affiliations

  1. IBM Almaden Research Center, 650 Harry Road, San Jose, CA, 95120, USA

    Fei Wang

  2. Department of CISE, University of Florida, Gainesville, FL, 32611, USA

    Baba C. Vemuri

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  1. Fei Wang
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  2. Baba C. Vemuri
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Wang, F., Vemuri, B.C. Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy. Int J Comput Vision 74, 201–215 (2007). https://doi.org/10.1007/s11263-006-0011-2

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