Optimizing wavelets for the analysis of fMRI data

Manuela Feilner; Thierry Blu; Michael A. Unser

doi:10.1117/12.408652

4 December 2000 Optimizing wavelets for the analysis of fMRI data

Manuela Feilner, Thierry Blu, Michael A. Unser

Proceedings Volume 4119, Wavelet Applications in Signal and Image Processing VIII; (2000) https://doi.org/10.1117/12.408652
Event: International Symposium on Optical Science and Technology, 2000, San Diego, CA, United States

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.

Citation Download Citation

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

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