Cross-manifold guidance in deformable registration of brain MR images

Jinpeng Zhang, Qian Wang, Guorong Wu, Dinggang Shen

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

3 Citations (Scopus)


Manifold is often used to characterize the high-dimensional distribution of individual brain MR images. The deformation field, used to register the subject with the template, is perceived as the geodesic pathway between images on the manifold. Generally, it is non-trivial to estimate the deformation pathway directly due to the intrinsic complexity of the manifold. In this work, we break the restriction of the single and complex manifold, by short-circuiting the subject-template pathway with routes from multiple simpler manifolds. Specifically, we reduce the anatomical complexity of the subject/template images, and project them to the virtual and simplified manifolds. The projected simple images then guide the subject image to complete its journey toward the template image space step by step. In the final, the subject-template pathway is computed by traversing multiple manifolds of lower complexity, rather than depending on the original single complex manifold only. We validate the cross-manifold guidance and apply it to brain MR image registration. We conclude that our method leads to superior alignment accuracy compared to state-of-the-art deformable registration techniques.

Original languageEnglish
Title of host publicationMedical Imaging and Augmented Reality - 7th International Conference, MIAR 2016, Proceedings
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319437743
Publication statusPublished - 2016
Externally publishedYes
Event7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016 - Bern, Switzerland
Duration: 2016 Aug 242016 Aug 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)03029743
ISSN (Electronic)16113349


Other7th International Conference on Medical Imaging and Augmented Reality, MIAR 2016

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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