![]() ![]() ![]() These are modeled by heavy-tail distributions because their histograms exhibit slower tail decay than the Gaussian. The empirical distributions of the signals on all the regions are computed in a compressed wavelet space. A frequency-adaptive wavelet shrinkage scheme is employed to obtain essentially optimal estimations of the signals in the wavelet space. An anatomical subvolume probabilistic atlas is used to tessellate the structural and functional signals into smaller regions each of which is processed separately. We use structural magnetic resonance imaging (MRI) and fMRI to empirically estimate the distribution of the wavelet coefficients of the data both across individuals and spatial locations. The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |