Topology preserving atlas construction from shape data without correspondence using sparse parameters

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Publication Type pre-print
School or College <blank>
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Creator Gerig, Guido
Other Author Durrleman, Stanley; Prastawa, Marcel; Korenberg, Julie R.; Joshi, Sarang; Trouve, Alain
Title Topology preserving atlas construction from shape data without correspondence using sparse parameters
Date 2012-01-01
Description Statistical analysis of shapes, performed by constructing an atlas composed of an average model of shapes within a population and associated deformation maps, is a fundamental aspect of medical imaging studies. Usual methods for constructing a shape atlas require point correspondences across subjects, which are difficult in practice. By contrast, methods based on currents do not require correspondence. However, existing atlas construction methods using currents suffer from two limitations. First, the template current is not in the form of a topologically correct mesh, which makes direct analysis on shapes difficult. Second, the deformations are parametrized by vectors at the same location as the normals of the template current which often provides a parametrization that is more dense than required. In this paper, we propose a novel method for constructing shape atlases using currents where topology of the template is preserved and deformation parameters are optimized independently of the shape parameters. We use an L1-type prior that enables us to adaptively compute sparse and low dimensional parameterization of deformations.We show an application of our method for comparing anatomical shapes of patients with Down's syndrome and healthy controls, where the sparse parametrization of diffeomorphisms decreases the parameter dimension by one order of magnitude.
Type Text
Publisher Springer
Volume 7512
First Page 223
Last Page 230
Language eng
Bibliographic Citation Durrleman, S., Prastawa, M., Korenberg, J. R., Joshi, S., Trouve, A., & Gerig, G. (2012). Topology preserving atlas construction from shape data without correspondence using sparse parameters. Lecture Notes in Computer Science (LNCS), 7512, 223-30.
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/10.1007/978-3-642-33454-2_28.
Format Medium application/pdf
Format Extent 2,310,686 bytes
Identifier uspace,19154
ARK ark:/87278/s6x09h5r
Setname ir_uspace
Date Created 2015-02-20
Date Modified 2015-02-27
ID 712828
Reference URL https://collections.lib.utah.edu/ark:/87278/s6x09h5r
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