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

Jing Wang; C-I Chang; Hsiang Ming Chen; C -C Chen; Jyh Wen Chai; Yen-Chieh Ouyang

doi:10.1109/IEMBS.2005.1617133

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

Conf Proc IEEE Eng Med Biol Soc. 2005:2005:3108-11. doi: 10.1109/IEMBS.2005.1617133.

Authors

Jing Wang¹, C-I Chang, Hsiang Ming Chen, C -C Chen, Jyh Wen Chai, Yen-Chieh Ouyang

Affiliation

¹ Remote Sensing Signal and Image Processing Laboratory, Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250.

PMID: 17282902
DOI: 10.1109/IEMBS.2005.1617133

Abstract

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 under-complete as opposed to the latter which uses over-complete ICA. Such a fundamental difference results in completely different applications.