Geodesic image regression with a sparse parameterization of diffeomorphisms

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Publication Type pre-print
School or College <blank>
Department <blank>
Creator Gerig, Guido
Other Author Fishbaugh, James.; Prastawa, Marcel; Durrleman, Stanley
Title Geodesic image regression with a sparse parameterization of diffeomorphisms
Date 2013-01-01
Description Image regression allows for time-discrete imaging data to be modeled continuously, and is a crucial tool for conducting statistical analysis on longitudinal images. Geodesic models are particularly well suited for statistical analysis, as image evolution is fully characterized by a baseline image and initial momenta. However, existing geodesic image regression models are parameterized by a large number of initial momenta, equal to the number of image voxels. In this paper, we present a sparse geodesic image regression framework which greatly reduces the number of model parameters. We combine a control point formulation of deformations with a L1 penalty to select the most relevant subset of momenta. This way, the number of model parameters reflects the complexity of anatomical changes in time rather than the sampling of the image. We apply our method to both synthetic and real data and show that we can decrease the number of model parameters (from the number of voxels down to hundreds) with only minimal decrease in model accuracy. The reduction in model parameters has the potential to improve the power of ensuing statistical analysis, which faces the challenging problem of high dimensionality.
Type Text
Publisher Springer
Volume 8085
First Page 95
Last Page 102
Language eng
Bibliographic Citation Fishbaugh, J., Prastawa, M., Gerig, G., & Durrleman, S. (2013). Geodesic image regression with a sparse parameterization of diffeomorphisms. Proceedings of the Geometric Science of Information Conference (GSI), 8085, 95-102.
Rights Management (c) Springer (The original publication is available at www.springerlink.com) ; The final publication is available at Springer via http://link.springer.com/chapter/10.1007%2F978-3-642-40020-9_9 ; Doi 10.1007/978-3-642-40020-9_9
Format Medium application/pdf
Format Extent 1,134,175 bytes
Identifier uspace,18998
ARK ark:/87278/s6dn7f52
Setname ir_uspace
Date Created 2014-11-06
Date Modified 2014-11-06
ID 712708
Reference URL https://collections.lib.utah.edu/ark:/87278/s6dn7f52
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