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Proceedings Paper

Correction scheme for multiple correlated statistical tests in local shape analysis
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Paper Abstract

In neuroimaging research, shape analysis has become a field of great interest due to the ability to locate morphological brain changes between different groups. Currently, many local shape analysis approaches fail to correct for their high number of correlated statistical tests. This can result in an overly optimistic estimate of the local shape analysis. This paper presents a correction scheme for objects described by the parametrized 3D closed surface description SPHARM. The SPHARM parameterization was determined via an area preserving, distortion minimizing optimization. The correction scheme decomposes the object surface into overlapping planar images via a cylindrical equal area projection of the parameterization. The images are individually analyzed with the SnPM/SPM package using a voxel-level non-parametric multiple testing procedure based on permutation tests. The correction scheme employs conservative tests resulting in a pessimistic estimate. We present an application of the correction scheme to the shape similarity analysis of lateral ventricles

Paper Details

Date Published: 12 May 2004
PDF: 8 pages
Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.533026
Show Author Affiliations
Martin A. Styner, Univ. of Bern (Switzerland)
Guido Gerig, Univ. of North Carolina/Chapel Hill (United States)


Published in SPIE Proceedings Vol. 5370:
Medical Imaging 2004: Image Processing
J. Michael Fitzpatrick; Milan Sonka, Editor(s)

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