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Near‐optimal region selection for feature space reduction: novel preprocessing methods for classifying MR spectra
Author(s) -
Nikulin Alexander E.,
Dolenko Brion,
Bezabeh Tedros,
Somorjai Ray L.
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<209::aid-nbm510>3.0.co;2-5
Subject(s) - preprocessor , pattern recognition (psychology) , artificial intelligence , feature selection , computer science , feature extraction , identification (biology) , selection (genetic algorithm) , feature (linguistics) , reduction (mathematics) , astrocytoma , mathematics , biology , glioma , genetics , linguistics , philosophy , botany , geometry
We introduce a global feature extraction method specifically designed to preprocess magnetic resonance spectra of biomedical origin. Such preprocessing is essential for the accurate and reliable classification of diseases or disease stages manifest in the spectra. The new method is genetic algorithm‐guided. It is compared with our enhanced version of the standard forward selection algorithm. Both seek and select optimal spectral subregions. These subregions necessarily retain spectral information, thus aiding the eventual identification of the biochemistry of disease presence and progression. The power of the methods is demonstrated on two biomedical examples: the discrimination between meningioma and astrocytoma in brain tissue biopsies, and the classification of colorectal biopsies into normal and tumour classes. Both preprocessing methods lead to classification accuracies over 97% for the two examples. © 1998 John Wiley & Sons, Ltd.