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Independent Component Analysis-Applications In Multispectral Brain Magnetic Resonance Image Analysis
|關鍵字:||Independent component analysis (ICA);獨立成分分析;Over-complete ICA (OC-ICA);Band Expansion Process (BEP);Support vector machine (SVM);完備性獨立成分分析;擴維度;支援向量機;費雪線性區別法||出版社:||電機工程學系所||引用:||1.G.A. Wright, “Magnetic resonance imaging,” IEEE Signal Processing Magazine, 14(1): 56-66, 1997. 2.L. Clarke, R. Vethuizen, M. Camacho, J. Heine, M. Vaidyanathan, L.O. Hall, R.W. Thatcher and M.L. Silbiger, “MRI segmentation: methods and applications,” Magnetic Resonance Imaging, 13(3): 343-368, 1995. 3.J.C. Bezdek, L.O. Hall and L. Clarke, “Review of MRI segmentation techniques using pattern recognition,” Medical Physics, 20: 1033-1048, 1993. 4.J.P. Windham, M.A. Abd-Allah, D.A. Reimann, J.W. Froehich and A.M. Haggar, “Eigenimage filtering in MR imaging,“ Journal of Computer Assisted Tomography, 12(1): 1-9, 1988. 5.A.M. Haggar, J.P. Windham D.A. Reimann, D.C. Hearshen and J.W. 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Patz, “The Rician Distribution of Noisy MRI Data,” Magnetic Resonance in Medicine, 34: 910-914, 1995. 45.Y. Wu, S. K. Warfield, I. L. Tan, W. M. Wells III, D. S. Meier, R. A. van Schijndel, F. Barkhof, and C. R.G. Guttmann, “Automated segmentation of multiple sclerosis lesion subtypes with multichannel MRI,” NeuroImage, 32: 1205-1215, 2006.||摘要:||
Independent component analysis (ICA) has found great promise in magnetic resonance (MR) image analysis where signal sources in MR images can be separated by the ICA via its produced Independent Components (ICs). Unfortunately, in order to apply ICA effectively for MR image analysis two key issues must be addressed but have been overlooked in the past. One is the lack of MR images to be used to unmix signal sources of interest, referred to as over-complete ICA (OC-ICA). Another is the use of random initial projection vectors by ICA which causes inconsistent results. This dissertation explores ICA applications in brain MR image classification while addressing the two issues. Since the ICA is a source separation technique and is not developed for classification, this dissertation first develops two ICA-based classification techniques which implements the over-complete ICA in conjunction with spatial domain-based classification, such as Fisher's linear discriminant analysis (FLDA) and Support vector machine (SVM), so as to achieve better classification in each of ICA-demixed ICs. Surprisingly, experimental results show that with the help of classification, the OC-ICA performs significantly better in terms of classification of three major brain tissue substances, WM, GM and CSF. Despite that the three-class classification may appear in different orders resulting from a random order that ICs are generated, such a random appearing order has very little effect on classification results. Secondly, in order to address the issue of insufficient MR band images, a band expansion process (BEP) is proposed and developed 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. Thirdly, to resolve the issue of randomly initial projection vectors used by the ICA, 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. The BEP and PICA are combined to further develop a new ICA-based approach, referred to as PICA-BEP to perform MR image analysis. The experimental results show that PICA-ICA improves the classification performance of traditional ICA approaches and spatial domain-based analysis techniques such as C-means. It has been shown that the brain skull interference which can significantly hinder MR image analysis. So, for the ICA to be effective it needs an additional IC to accommodate the brain skull. Unfortunately, this requirement will further deteriorate the ICA performance due to the lack of MR band images. In order to resolve this dilemma, this dissertation concludes with developing a skull stripping process as a preprocessing so that the brain skull effect can be minimized to enhance the ability of ICA in source separation.
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