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Proceedings Paper

Features selection for clustering of fMRI data
Author(s): Francois G. Meyer; Jatuporn Chinrungrueng
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Paper Abstract

We address the problem of the analysis of event-related functional Magnetic Resonance Images (fMRI). We propose to separate the fMRI time series into "activated" and "non-activated" clusters. The fMRI time series are projected onto a basis, and the clustering is performed using the coefficients in that basis. We developed a new algorithm to select that basis which provides the optimal clustering of the time series. Our approach does not require any training datasets or any model of the hemodynamic response. The basis is constructed using a dictionary of wavelet packets. We search for the optimal basis in this dictionary using a new cost function that measures the clustering power of a set of wavelet packets. Our approach can be easily extended to classification problems. We have conducted several experiments with synthetic and in-vivo event-related fMRI data. Our method is capable of discovering the structures of the synthetic data. The method also successfully detected activated voxels in the in-vivo fMRI.

Paper Details

Date Published: 13 November 2003
PDF: 9 pages
Proc. SPIE 5207, Wavelets: Applications in Signal and Image Processing X, (13 November 2003); doi: 10.1117/12.507494
Show Author Affiliations
Francois G. Meyer, Univ. of Colorado/Boulder (United States)
Jatuporn Chinrungrueng, Univ. of Colorado/Boulder (United States)


Published in SPIE Proceedings Vol. 5207:
Wavelets: Applications in Signal and Image Processing X
Michael A. Unser; Akram Aldroubi; Andrew F. Laine, Editor(s)

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