De-noising of Functional Magnetic Resonance Imaging (fMRI) data using Nonlinear Anisotropic 1D and 2D filters
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Abstract
Functional magnetic resonance imaging (fMRI) is a noninvasive technique used for measuring the functionality of the human brain.
Functional MR data suffers from low SNR due to Rician noise. Since this noise is multiplicative in nature which makes further processing of the data
a challenging job. A number of conventional filters have been used for de-noising this low SNR data. In this work nonlinear anisotropic 1D and 2D
filters are applied to simulated and actual fMRI data. A hybrid filter which consists of serial filtering by 1D and 2D filters is proposed in this work.
Correlation results of the proposed filtered data show that its performance is better in terms of correlation from 1D and 2D filters.
Keyword: fMRI de-noising, Rician noise, Anisotropic filters
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