Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy |
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Authors: | Fei Wang Baba C Vemuri |
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Affiliation: | (1) IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120, USA;(2) Department of CISE, University of Florida, Gainesville, FL 32611, USA |
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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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Keywords: | information theory Shannon entropy multi-modal non-rigid registration B-splines |
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