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Two Dimensional Spatial Independent Component Analysis and Its Application in fMRI Data Process
作者姓名:CHEN  Hua-fu  YAO  De-zhong
作者单位:[1]School of Life Science and Technology, University of Electronic Science and Technology of China Chengdu 610054 China [2]School of Applied Mathematics, University of Electronic Science and Technology of China Chengdu 610054 China
基金项目:the 973 Project (No.2003CB716106), NSFC (No.90205003, 30200059), TRAP0YT, Doctor Training Fund of M0E, PRC, Key Research Project of Science and Technology of M0E, Fok Ying Tong Education Foundation (No.91041).
摘    要:One important application of independent component analysis (ICA) is in image processing. A two dimensional (2-D) composite ICA algorithm framework for 2-D image independent component analysis (2-D ICA) is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional (2-D) image deta and one-dimensional (l-D) signal. Moreover the combination of the Newton (fixed-point algorithm) and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging (fMRI) show the potential application of composite 2-D ICA in the brain activity detection.

关 键 词:无约束成分分析  图象处理  2-D  ICA算法  图象编码
收稿时间:2004-10-09

Two Dimensional Spatial Independent Component Analysis and Its Application in fMRI Data Process
CHEN Hua-fu YAO De-zhong.Two Dimensional Spatial Independent Component Analysis and Its Application in fMRI Data Process[J].Journal of Electronic Science Technology of China,2005,3(3):231-233,237.
Authors:CHEN Hua-fu  Yao De-zhong
Abstract:One important application of independent component analysis (ICA) is in image processing. A two dimensional (2-D) composite ICA algorithm framework for 2-D image independent component analysis (2-D ICA) is proposed. The 2-D nature of the algorithm provides it an advantage of circumventing the roundabout transforming procedures between two dimensional (2-D) image data and one-dimensional (1-D) signal. Moreover the combination of the Newton (fixed-point algorithm) and natural gradient algorithms in this composite algorithm increases its efficiency and robustness. The convincing results of a successful example in functional magnetic resonance imaging (fMRI) show the potential application of composite 2-D ICA in the brain activity detection.
Keywords:independent component analysis  image processing  composite 2-D ICA algorithm  functional magnetic resonance imaging
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