Please use this identifier to cite or link to this item:
標題: Band expansion-based over-complete independent component analysis for multispectral processing of magnetic resonance images
作者: Ouyang, Y.C.
Chen, H.M.
Chai, J.W.
Chen, C.C.C.
Poon, S.K.
Yang, C.W.
Lee, S.K.
Chang, C.I.
關鍵字: band-expansion process (BEP);FastICA;independent component analysis;(ICA);magnetic resonance (MR);analysis;over-complete ICA (OC-ICA);prioritized ICA (PICA);prioritized ICA band-expansion process;(PICA-BEP);subspace projection approach;classification
Project: Ieee Transactions on Biomedical Engineering
期刊/報告no:: Ieee Transactions on Biomedical Engineering, Volume 55, Issue 6, Page(s) 1666-1677.
Independent component analysis (ICA) has found great promise in magnetic resonance (MR) image analysis. Unfortunately, two key issues have been overlooked and not investigated. One is the lack of MR images to be used to unmix signal sources of interest. Another is the use of random initial projection vectors by ICA, which causes inconsistent results. In order to address the first issue, this paper introduces a band-expansion process (BEP) to generate an additional new set of images from the original MR images via nonlinear functions. These newly generated images are then combined with the original MR images to provide sufficient MR images for ICA analysis. In order to resolve the second issue, a prioritized ICA (PICA) is designed to rank the ICA-generated independent components (ICs) so that MR brain tissue substances can be unmixed and separated by different ICs in a prioritized order. Finally, BEP and PICA are combined to further develop a new ICA-based approach, referred to as PICA-BEP to perform MR image analysis.
ISSN: 0018-9294
DOI: 10.1109/tbme.2008.919107
Appears in Collections:電機工程學系所

Show full item record

Google ScholarTM




Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.