From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods.
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 addressed: (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.
Karolinska MR-Research Center, Stockholm University PET-center, Sweden.
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Hagberg
D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods; D009369:Neoplasms / Q000145:classification* / Q000473:pathology; D019906:Nuclear Magnetic Resonance, Biomolecular / Q000379:methods*; D010363:Pattern Recognition, Automated