STATISTICAL SHAPE ANALYSIS OF BRAIN STRUCTURES USING SPHERICAL WAVELETS

Author

Nain, D. ; Styner, M. ; Niethammer, M. ; Levitt, J.J. ; Shenton, M.E. ; Gerig, G. ; Bobick, A. ; Tannenbaum, A.

Author_Institution

Coll. of Comput., Georgia Tech., Atlanta, GA

fYear

2007

fDate

12-15 April 2007

Firstpage

209

Lastpage

212

Abstract

We present a novel method of statistical surface-based morphometry based on the use of non-parametric permutation tests and a spherical wavelet (SWC) shape representation. As an application, we analyze two brain structures, the caudate nucleus and the hippocampus, and compare the results obtained to shape analysis using a sampled point representation. Our results show that the SWC representation indicates new areas of significance preserved under the FDR correction for both the left caudate nucleus and left hippocampus. Additionally, the spherical wavelet representation provides a natural way to interpret the significance results in terms of scale in addition to knowing the spatial location of the regions

Keywords

biomedical MRI; brain; image segmentation; medical image processing; neurophysiology; statistical analysis; wavelet transforms; FDR correction; MRI images; binary segmentation; brain structures; caudate nucleus; hippocampus; nonparametric permutation tests; sampled point representation; shape analysis; spherical wavelet shape representation; spherical wavelets; statistical shape analysis; statistical surface-based morphometry; Biomedical computing; Biomedical imaging; Brain; Hippocampus; Psychiatry; Shape; Surface morphology; Surface waves; Testing; Wavelet analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging: From Nano to Macro, 2007. ISBI 2007. 4th IEEE International Symposium on

Conference_Location

Arlington, VA

Print_ISBN

1-4244-0672-2

Electronic_ISBN

1-4244-0672-2

Type

conf

DOI

10.1109/ISBI.2007.356825

Filename

4193259