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A Hyperspectral Imaging Approach to Unsupervised Magnetic Resonance Brain Tissue Classification
|關鍵字:||Pixel Purity Index;純度像素索引;Magnetic Resonance Imaging;磁振造影像||出版社:||電機工程學系所||引用:||Reference  G.. A. Wright, “Magnetic resonance imaging,” IEEE Signal Processing Magazine, 14(1): 56-66, 1997  C.-I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic Publishers, 2003.  J. W. Boardman, F. A. Kruse and R. O. Green, “Mapping target signatures via partial unmixing of AVIRIS data,” Summaries of JPL Airborne Earth Science Workshop, Pasadena, CA, 1995  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.  J. C. Bezdek, L. O. Hall and L. Clarke, “Review of MRI segmentation techniques using pattern recognition,” Medical Physics, 20: 1033-1048, 1993.  A. Hyvarinen, J. Karhunen and E. Oja, Independent Component Analysis, New York: John Wiley & Sons, 2001.  T. Nakai, S. Muraki, E. Bagarinao, Y. 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Hyperspectral imaging approach is an emerging remote sensing technique which has recently found applications in magnetic resonance imaging. In this thesis, we develop a new application of using a widely used endmember extraction algorithm, called pixel purity index (PPI) to find training samples directly from the data without prior knowledge for MR brain image classification. Two major challenging issues arise in unsupervised classification. One is how to generate desired knowledge directly from the data in an unsupervised manner. The other is how to find an appropriate follow-up classifier to use the obtained unsupervised knowledge to perform supervised classification. This study presents a new approach to unsupervised classification for magnetic resonance imaging. To address the first issue the pixel purity index (PPI) which is commonly used in hyperspeftral imaging for endmember extraction is used to find a good set of initial training samples without prior knowledge. To address the second issue the PPI-found samples are then used as training samples for a support vector machine to find a good set of training samples for a follow-up supervised classifier, Fisher's linear discriminate analysis (FLDA) which performs classification iteratively to produce final results. The experimental results show the proposed approach has great promise in unsupervised classification.
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