✦ LIBER ✦

From magnetic resonance spectroscopy to classification of tumors. A review of pattern recognition methods

✍ Scribed by Gisela Hagberg

Publisher: John Wiley and Sons
Year: 1998
Tongue: English
Weight: 104 KB
Volume: 11
Category: Article
ISSN: 0952-3480
DOI: 10.1002/(sici)1099-1492(199806/08)11:4/5<148::aid-nbm511>3.0.co;2-4

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✦ Synopsis

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.

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