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From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods
Author(s) -
Hagberg Gisela
Publication year - 1998
Publication title -
nmr in biomedicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.278
H-Index - 114
eISSN - 1099-1492
pISSN - 0952-3480
DOI - 10.1002/(sici)1099-1492(199806/08)11:4/5<148::aid-nbm511>3.0.co;2-4
Subject(s) - linear discriminant analysis , principal component analysis , normalization (sociology) , pattern recognition (psychology) , artificial intelligence , computer science , multivariate statistics , nuclear magnetic resonance , multivariate analysis , mathematics , physics , machine learning , sociology , anthropology
This article reviews the wealth of different pattern recognition methods that have been used for magnetic resonance spectroscopy (MRS) based tumor classification. The methods have in common that the entire MR spectra is used to develop linear and non‐linear classifiers. The following issues are adressed: (i) pre‐processing, such as normalization and digitization, (ii) extraction of relevant spectral features by multivariate methods, such as principal component analysis, linear discriminant analysis (LDA), and optimal discriminant vector, and (iii) classification by LDA, cluster analysis and artificial neural networks. Different approaches are compared and discussed in view of practical and theoretical considerations. © 1998 John Wiley & Sons, Ltd.