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An Over-Complete Independent Component Analysis (ICA) Approach to Magnetic Resonance Image Analysis

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6 Author(s)
Jing Wang ; Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250 ; C. -I. Chang ; Hsiang Ming Chen ; C. C. -C. Chen
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This paper presents a new application of independent component analysis (ICA) in magnetic resonance (MR) image analysis. One of most successful applications for ICA-based approaches in MR imaging is functional MRI (fMRI) which basically deals with one-dimensional temporal signals. The ICA approach proposed in this paper is rather different and considers a set of MR images acquired by different pulse sequences as a 3-dimensional image cube and performs image analysis rather than signal analysis. One major difference between the fMRI-based ICA approaches and our proposed ICA-based image analysis is that the ICA used in the former is undercomplete as opposed to the latter which uses over-complete ICA. Such a fundamental difference results in completely different applications

Published in:

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference

Date of Conference:

17-18 Jan. 2006