Abstract
With six or more tissue parameters MR imaging has substantial theoretical potential for tissue discrimination in different organs. Accurate tissue discrimination may require dependence on computer-based image analysis techniques to extract tissue-specific information. Our approach to these problems has been through pattern classification techniques (Alaux 1988, 1989). Our routine analysis has included: principal components analysis, supervised Bayesian classification, and clustering techniques (minimum distance and dynamic clustering).
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References
Alaux A (1988) Classification of MR images. Trondheim
Alaux A (1989) Pattern classification techniques. Mons
Alaux A, Dalati M (1988) Multispectral analysis of MR images. Second European Congress of NMR in Medicine and Biology, Berlin. Book of Abstracts, 36
Alaux A, Dalati M, Rinck P (1989 a) Multispectral analysis of magnetic resonance images. International Congress of Radiology, Paris, 1–8 July 1989. Book of Abstracts, 266
Alaux A, Dalati M, Rinck P (1989 b) Pattern recognition technique in cardiac magnetic resonance imaging. International Congress of Radiology, Paris 1–8 July 1989. Book of Abstract, 730
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© 1990 Springer-Verlag Berlin Heidelberg
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Alaux, A., Rinck, P.A. (1990). Multispectral Analysis of Magnetic Resonance Images: A Comparison Between Supervised and Unsupervised Classification Techniques. In: Higer, H.P., Bielke, G. (eds) Tissue Characterization in MR Imaging. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-74993-3_26
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DOI: https://doi.org/10.1007/978-3-642-74993-3_26
Publisher Name: Springer, Berlin, Heidelberg
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