Image registration driven by combined probabilistic and geometric descriptors

Update item information
Publication Type pre-print
School or College <blank>
Department <blank>
Creator Gerig, Guido
Other Author Ha, Linh.; Prastawa, Marcel.; Gilmore, John H.; Silva, Claudio T.; Joshi, Sarang
Title Image registration driven by combined probabilistic and geometric descriptors
Date 2010-01-01
Description Deformable image registration in the presence of considerable contrast dierences and large-scale size and shape changes represents a signicant 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 myeli-nation 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.
Type Text
Publisher Springer
Volume 6362/2010
First Page 602
Last Page 609
Language eng
Bibliographic Citation Ha, L., Prastawa, M. W., Gerig, G., Gilmore, J. H., Silva, C. T., & Joshi, S. (2010). Image registration driven by combined probabilistic and geometric descriptors. MICCAI 2010, Lecture Notes in Computer Science (LNCS), 6362/2010, 602-9.
Rights Management ((c) Springer (The original publication is available at www.springerlink.com) The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-15745-5_74.
Format Medium application/pdf
Format Extent 2,721,277 bytes
Identifier uspace,19208
ARK ark:/87278/s6dv4v15
Setname ir_uspace
Date Created 2015-03-06
Date Modified 2015-03-12
ID 712855
Reference URL https://collections.lib.utah.edu/ark:/87278/s6dv4v15
Back to Search Results