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    Algebraic Methods for Direct and Feature Based Registration of Diffusion Tensor Images

    Algebraic Methods for Direct and Feature Based Registration of Diffusion Tensor Images

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    Abstract We present an algebraic solution to both direct and feature-based registration of diffusion tensor images under various local deformation models. In the direct case, we show how to linearly recover a local deformation from the partial derivatives of the tensor using the so-called Diffusion Tensor Constancy Constraint, a generalization of the brightness constancy constraint to diffusion tensor data. In the feature-based case, we show that the tensor reorientation map can be found in closed form by exploiting the spectral properties of the rotation group. Given this map, solving for an affine deformation becomes a linear problem. We test our approach on synthetic, brain and heart diffusion tensor images.

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    Titel :

    Algebraic Methods for Direct and Feature Based Registration of Diffusion Tensor Images

    Beteiligte:
    Goh, Alvina (Autor:in) / Vidal, René (Autor:in)
    Erschienen in:

    Computer Vision – ECCV 2006 ; 8 ; 514-525

    Lecture Notes in Computer Science ; 3953 , 8

    Erscheinungsdatum :

    2006-01-01

    Format / Umfang :

    12 pages

    ISBN :

    978-3-540-33837-6 , 978-3-540-33836-9

    ISSN :

    0302-9743 , 1611-3349

    DOI :

    https://doi.org/10.1007/11744078_40

    Medientyp :

    Aufsatz/Kapitel (Buch)

    Format :

    Elektronische Ressource

    Sprache :

    Englisch

    Schlagwörter :

    Singular Value Decomposition , Deformation Model , Point Correspondence , Single Voxel , Registration Problem Computer Science , Image Processing and Computer Vision , Pattern Recognition , Computer Graphics , Artificial Intelligence

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