EPFL
 Biomedical Imaging GroupSTI
EPFL
  Publications
English only   BIG > Publications > Wavelet SPM


 CONTENTS
 Home Page
 News & Events
 People
 Publications
 Tutorials and Reviews
 Research
 Demos
 Download Algorithms

 DOWNLOAD
 PDF
 Postscript
 All BibTeX References

WSPM: Wavelet-Based Statistical Parametric Mapping

D. Van De Ville, M.L. Seghier, F. Lazeyras, T. Blu, M. Unser

NeuroImage, vol. 37, no. 4, pp. 1205-1217, October 1, 2007.



Recently, we have introduced an integrated framework that combines wavelet-based processing with statistical testing in the spatial domain. In this paper, we propose two important enhancements of the framework. First, we revisit the underlying paradigm; i.e., that the effect of the wavelet processing can be considered as an adaptive denoising step to “improve” the parameter map, followed by a statistical detection procedure that takes into account the non-linear processing of the data. With an appropriate modification of the framework, we show that it is possible to reduce the bias of the method with respect to the best linear estimate, providing conservative results that are closer to the original data. Second, we propose an extension of our earlier technique that compensates for the lack of shift-invariance of the wavelet transform. We demonstrate experimentally that both enhancements have a positive effect on performance. In particular, we present a reproducibility study for multi-session data that compares WSPM against SPM with different amounts of smoothing. The full approach is available as a toolbox, named WSPM, for the SPM2 software; it takes advantage of multiple options and features of SPM such as the general linear model.

The associated software is available here.


@ARTICLE(http://bigwww.epfl.ch/publications/vandeville0704.html,
AUTHOR="Van De Ville, D. and Seghier, M.L. and Lazeyras, F. and Blu, T.
        and Unser, M.",
TITLE="{WSPM}: {W}avelet-Based Statistical Parametric Mapping",
JOURNAL="NeuroImage",
YEAR="2007",
volume="37",
number="4",
pages="1205--1217",
month="October 1,",
note="")

© 2007 Elsevier. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from Elsevier.
This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.