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In functional magnetic resonance imaging (fMRI) data, activated voxels are usually very small in number and are embedded in a mass of inactive voxels. For clustering analysis, this situation generates an ill-balanced data problem among different classes of voxels. In this paper we propose a novel method to overcome the ill-balanced data problem, by reducing the number of voxels to be processed by...
We present an adaptive smoothing scheme for denoising functional magnetic resonance imaging (fMRI) data using weighted average filtering. A novel metric is proposed that assigns the weights of the smoothing kernel on the basis of similarity of the voxels under the smoothing kernel with the voxel under consideration as well as a reference time course. Pearson's coefficient of correlation is used as...
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