Image registration driven by combined probabilistic and geometric descriptors

Linh Ha, Marcel Prastawa, Guido Gerig, John H. Gilmore, Cláudio T. Silva, Sarang Joshi

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

Abstract

Deformable image registration in the presence of considerable contrast differences and large-scale size and shape changes represents a significant challenge for image registration. A representative driving application is the study of early brain development in neuroimaging, which requires co-registration of images of the same subject across time or building 4-D population atlases. Growth during the first few years of development involves significant changes in size and shape of anatomical structures but also rapid changes in tissue properties due to myelination and structuring that are reflected in the multi-modal Magnetic Resonance (MR) contrast measurements. We propose a new registration method that generates a mapping between brain anatomies represented as a multi-compartment model of tissue class posterior images and geometries. We transform intensity patterns into combined probabilistic and geometric descriptors that drive the matching in a diffeomorphic framework, where distances between geometries are represented using currents which does not require geometric correspondence. We show preliminary results on the registrations of neonatal brain MRIs to two-year old infant MRIs using class posteriors and surface boundaries of structures undergoing major changes. Quantitative validation demonstrates that our proposed method generates registrations that better preserve the consistency of anatomical structures over time.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
PublisherSpringer Verlag
Pages602-609
Number of pages8
EditionPART 2
ISBN (Print)3642157440, 9783642157448
DOIs
StatePublished - 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6362 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Ha, L., Prastawa, M., Gerig, G., Gilmore, J. H., Silva, C. T., & Joshi, S. (2010). Image registration driven by combined probabilistic and geometric descriptors. In Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings (PART 2 ed., pp. 602-609). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6362 LNCS, No. PART 2). Springer Verlag. https://doi.org/10.1007/978-3-642-15745-5_74