Translator Disclaimer
4 December 2000 Optimizing wavelets for the analysis of fMRI data
Author Affiliations +
Abstract
Ruttiman et al. Have proposed to use the wavelet transform for the detection and localization of activation patterns in functional magnetic resonance imaging (fMRI). Their main idea was to apply a statistical test in the wavelet domain to detect the coefficients that are significantly different form zero. Here, we improve the original method in the case of non-stationary Gaussian noise by replacing the original z-test by a t-test that takes into account the variability of each wavelet coefficient separately. The application of a threshold that is proportional to the residual noise level. After the reconstruction by an inverse wavelet transform, further improves the localization of the activation pattern in the spatial domain.
© (2000) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Manuela Feilner, Thierry Blu, and Michael A. Unser "Optimizing wavelets for the analysis of fMRI data", Proc. SPIE 4119, Wavelet Applications in Signal and Image Processing VIII, (4 December 2000); https://doi.org/10.1117/12.408652
PROCEEDINGS
12 PAGES


SHARE
Advertisement
Advertisement
RELATED CONTENT

Modulation classification using wavelet transform
Proceedings of SPIE (October 10 1994)
Which wavelet bases are the best for image denoising?
Proceedings of SPIE (September 17 2005)
Wavelets versus resels in the context of fMRI establishing...
Proceedings of SPIE (November 12 2003)
Wavelet ANOVA and fMRI
Proceedings of SPIE (October 25 1999)

Back to Top