Laplacian eigenmaps for multimodal groupwise image registration

Mathias Polfliet, Stefan Klein, Wiro J. Niessen, Jef Vandemeulebroucke

    Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

    1 Citation (Scopus)
    19 Downloads (Pure)


    Multimodal groupwise registration has been of growing interest to the image processing community due to developments in scanner technologies (e.g. multiparametric MRI, DCE-CT or PET-MR) that increased both the number of modalities and number of images under consideration. In this work a novel methodology is presented for multimodal groupwise registration that is based on Laplacian eigenmaps, a nonlinear dimensionality reduction technique. Compared to recently proposed dissimilarity metrics based on principal component analysis, the proposed metric should enable a better capture of the intensity relationships between different images in the group. The metric is constructed to be the second smallest eigenvalue from the eigenvector problem defined in Laplacian eigenmaps. The method was validated in three distinct experiments: a non-linear synthetic registration experiment, the registration of quantitative MRI data of the carotid artery, and the registration of multimodal data of the brain (RIRE). The results show increased accuracy and robustness compared to other state-of-the-art groupwise registration methodologies.

    Original languageEnglish
    Title of host publicationMedical Imaging 2017: Image Processing
    EditorsMartin A. Styner, Elsa D. Angelini
    Place of PublicationBellingham, WA, USA
    Number of pages7
    ISBN (Electronic)978-1-510607118
    Publication statusPublished - 2017
    EventMedical Imaging 2017: Image Processing - Orlando, United States
    Duration: 12 Feb 201714 Feb 2017

    Publication series

    NameProceedings of SPIE
    ISSN (Electronic)1605-7422


    ConferenceMedical Imaging 2017: Image Processing
    CountryUnited States


    • Algebraic connectivity
    • Groupwise registration
    • Laplacian eigenmaps
    • Multimodal registration

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