Please use this identifier to cite or link to this item:
|標題:||Independent component analysis for magnetic resonance image analysis||作者:||Ouyang, Y.C.
|關鍵字:||multispectral analysis;tissue classification;mri segmentation;sequences;model||Project:||Eurasip Journal on Advances in Signal Processing||期刊/報告no：:||Eurasip Journal on Advances in Signal Processing.||摘要:||
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 to demonstrate the proposed over-complete ICA, (OC-ICA) experiments are conducted for performance analysis and evaluation. Results show that the OC-ICA implemented with classification can be very effective, provided the training samples are judiciously selected. Copyright (c) 2008 Yen-Chieh Ouyang et al.
|Appears in Collections:||電機工程學系所|
Show full item record
TAIR Related Article
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.