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  Improved statistical power with a sparse shape model in detecting an aging effect in the hippocampus and amygdala

Chung, M. K., Kim, S.-G., Schaefer, S. M., van Reekum, C. M., Peschke-Schmitz, L., Sutterer, M. J., et al. (2014). Improved statistical power with a sparse shape model in detecting an aging effect in the hippocampus and amygdala. In Proceedings of Medical Imaging 2014: Image Processing. The International Society for Optical Engineering.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0018-BD74-3 Version Permalink: http://hdl.handle.net/21.11116/0000-0002-9E80-B
Genre: Conference Paper

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 Creators:
Chung, Moo K.1, Author
Kim, Seung-Goo2, Author              
Schaefer, Stacey M.1, Author
van Reekum, Carien M.3, Author
Peschke-Schmitz, Lara1, Author
Sutterer, Matthew J.4, Author
Davidson, Richard J.1, Author
Affiliations:
1University of Wisconsin, Madison, WI, USA, ou_persistent22              
2Methods and Development Group MEG and EEG - Cortical Networks and Cognitive Functions, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_2205650              
3University of Reading, Berkshire, United Kingdom, ou_persistent22              
4University of Iowa, Iowa City, IA, USA, ou_persistent22              

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Free keywords: Statistical power; Shape analysis; Sparse regression
 Abstract: The sparse regression framework has been widely used in medical image processing and analysis. However, it has been rarely used in anatomical studies. We present a sparse shape modeling framework using the Laplace- Beltrami (LB) eigenfunctions of the underlying shape and show its improvement of statistical power. Tradition- ally, the LB-eigenfunctions are used as a basis for intrinsically representing surface shapes as a form of Fourier descriptors. To reduce high frequency noise, only the first few terms are used in the expansion and higher frequency terms are simply thrown away. However, some lower frequency terms may not necessarily contribute significantly in reconstructing the surfaces. Motivated by this idea, we present a LB-based method to filter out only the significant eigenfunctions by imposing a sparse penalty. For dense anatomical data such as deformation fields on a surface mesh, the sparse regression behaves like a smoothing process, which will reduce the error of incorrectly detecting false negatives. Hence the statistical power improves. The sparse shape model is then applied in investigating the influence of age on amygdala and hippocampus shapes in the normal population. The advantage of the LB sparse framework is demonstrated by showing the increased statistical power.

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Language(s): eng - English
 Dates: 2014-03-21
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1117/12.2036497
 Degree: -

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Title: Medical Imaging 2014: Image Processing
Place of Event: San Diego, CA, USA
Start-/End Date: -

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Title: Proceedings of Medical Imaging 2014: Image Processing
Source Genre: Proceedings
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Affiliations:
Publ. Info: The International Society for Optical Engineering
Pages: - Volume / Issue: 9034 Sequence Number: 90340Y Start / End Page: - Identifier: -