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A multicomponent approach to nonrigid registration of diffusion tensor images

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Abstract

Diffusion tensor imaging has shown promise in the early detection and diagnosis of a host of disorders and neurologic conditions. In this paper, we propose a nonrigid registration approach for diffusion tensor images using a multicomponent information-theoretic measure. Explicit orientation optimization is enabled by incorporating tensor reorientation, which is necessary for wrapping diffusion tensor images. Experimental results on diffusion tensor images indicate the feasibility of the proposed approach and a much better performance compared to the affine registration method based on mutual information in terms of registration accuracy in the presence of geometric distortion.

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Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Mohammed Khader & A. Ben Hamza

  2. Escuela Superior de Ciencias Experimentales y Tecnologia, University of Rey Juan Carlos, Madrid, Spain

    Emanuele Schiavi

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  1. Mohammed Khader
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  2. Emanuele Schiavi
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  3. A. Ben Hamza
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Correspondence to A. Ben Hamza.

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Khader, M., Schiavi, E. & Hamza, A.B. A multicomponent approach to nonrigid registration of diffusion tensor images. Appl Intell 46, 241–253 (2017). https://doi.org/10.1007/s10489-016-0833-8

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  • DOI: https://doi.org/10.1007/s10489-016-0833-8

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