• 101331N

    Final published version, 502 KB, PDF document


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

    Research areas

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

ID: 33425541