Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2008, Article ID 780656, 14 pages doi:10.1155/2008/780656 Research Article Independent Component Analysis for Magnetic Resonance Image Analysis Yen-Chieh Ouyang,1 Hsian-Min Chen,1 Jyh-Wen Chai,2, 3, 4 Cheng-Chieh Chen,1 Clayton Chi-Chang Chen,4, 5 Sek-Kwong Poon,6 Ching-Wen Yang,7 and San-Kan Lee8 1 Department of Electrical Engineering, National Chung Hsing University, Taichung 402, Taiwan 2 Department of Radiology, College of Medicine, China Medical University, Taichung 404, Taiwan 3 School of Medicine, National Yang-Ming University, Taipei 112, Taiwan 4 Department of Radiology, Taichung Veterans General Hospital, Taichung 407, Taiwan 5 Department of Medical Imaging and Radiological Science, Central Taiwan University of Science and Technology, Taichung 406, Taiwan 6 Division of Gastroenterology, Department of Internal Medicine, Center of Clinical Informatics Research Development, Taichung Veterans General Hospital, Taichung 407, Taiwan 7 Computer Center, Taichung Veterans General Hospital, Taichung 407, Taiwan 8 Chia-Yi, Veterans Hospital, Chia-Yi 600, Taiwan Correspondence should be addressed to Clayton Chi-Chang Chen, ccc@mail.vghtc.gov.tw Received 11 October 2007 Revised 21 December 2007 Accepted 30 December 2007 Recommended by Chein-I Chang 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 �� 2008 Yen-Chieh Ouyang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. INTRODUCTION One of the greatest challenges in magnetic resonance (MR) image analysis is feature extraction of clinical information to be used for medical diagnosis. Unlike most medical modal- ities, the MRI is developed using tissue parameters such as spin-lattice (T1) and spin-spin (T2) relaxation times and proton density (PD) to characterize various tissue informa- tion at the same anatomical area [1]. As a result, the fea- tures extracted from MR images can be obtained by spatial domain-based information as well as tissue characterization information derived from different pulse sequences. There- fore, an effective feature extraction technique should take ad- vantage of both types of information. Over the past years, MR images are processed from two different perspectives. One is a traditional and general ap- proach which considers MR images as multidimensional data so that multivariate analysis can be applied. For ex- ample, in most applications MR images are processed as 3- dimenaional (3D) image cube with pixels replaced by voxels so that image processing techniques such as segmentation, region growing, classification, and pattern recognition are readily applied [2, 3]. In particular, a recent classification- based transform, called eigenimaging filter, has shown