DMCA
doi:10.1155/2008/780656 Research Article Independent Component Analysis for Magnetic Resonance Image Analysis
BibTeX
@MISC{Ouyang_doi:10.1155/2008/780656research,
author = {Yen-chieh Ouyang and Hsian-min Chen and Jyh-wen Chai and Cheng-chieh Chen and Clayton Chi-chang Chen and Sek-kwong Poon and Ching-wen Yang and San-kan Lee},
title = {doi:10.1155/2008/780656 Research Article Independent Component Analysis for Magnetic Resonance Image Analysis},
year = {}
}
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Abstract
Independent component analysis (ICA) has recently received considerable interest in applications of magnetic resonance (MR) image analysis. However, unlike its applications to functional magnetic resonance imaging (fMRI) where the number of data samples is greater than the number of signal sources to be separated, a dilemma encountered in MR image analysis is that the number of MR images is usually less than the number of signal sources to be blindly separated. As a result, at least two or more brain tissue substances are forced into a single independent component (IC) in which none of these brain tissue substances can be discriminated from another. In addition, since the ICA is generally initialized by random initial conditions, the final generated ICs are different. In order to resolve this issue, this paper presents an approach which implements the over-complete ICA in conjunction with spatial domain-based classification so as to achieve better classification in each of ICA-demixed ICs. In order
Keyphrases
magnetic resonance image analysis research article independent component analysis brain tissue substance signal source random initial condition spatial domain-based classification image analysis ica-demixed ic mr image analysis considerable interest independent component analysis data sample magnetic resonance single independent component functional magnetic resonance imaging over-complete ica mr image
