Large deformation diffeomorphic metric mapping of fiber orientations

Yan Cao, Michael I. Miller, Raimond L. Winslow, Laurent Younes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a method to match diffusion tensor magnetic resonance images (DT-MRI) through the large deformation diffeomorphic metric mapping of vector fields, focusing on the fiber orientations, considered as unit vector fields on the image volume. We study a suitable action of diffeomorphisms on such vector fields, and provide an extension of the Large Deformation Diffeomorphic Metric Mapping framework to this type ofdataset, resulting in optimizing for geodesies on the space of diffeomorphisms connecting two images. Two different distance function of vector fields are considered. Existence of the minimizers under smoothness assumptions on the compared vector fields is proved, and coarse to fine hierarchical strategies are detailed, to reduce both ambiguities and computation load. This is illustrated by numerical experiments on DT-MRI heart and brain images.

Original languageEnglish (US)
Title of host publicationProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
Pages1379-1386
Number of pages8
DOIs
StatePublished - Dec 1 2005
EventProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 - Beijing, China
Duration: Oct 17 2005Oct 20 2005

Publication series

NameProceedings of the IEEE International Conference on Computer Vision
VolumeII

Other

OtherProceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005
CountryChina
CityBeijing
Period10/17/0510/20/05

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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  • Cite this

    Cao, Y., Miller, M. I., Winslow, R. L., & Younes, L. (2005). Large deformation diffeomorphic metric mapping of fiber orientations. In Proceedings - 10th IEEE International Conference on Computer Vision, ICCV 2005 (pp. 1379-1386). [1544880] (Proceedings of the IEEE International Conference on Computer Vision; Vol. II). https://doi.org/10.1109/ICCV.2005.132