Premium
Automated feature extraction for the classification of human in vivo 13 C NMR spectra using statistical pattern recognition and wavelets
Author(s) -
Tate A. Rosemary,
Watson Des,
Eglen Stephen,
Arvanitis Theodores N.,
Thomas E. Louise,
Bell Jimmy D.
Publication year - 1996
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.1910350608
Subject(s) - pattern recognition (psychology) , artificial intelligence , linear discriminant analysis , principal component analysis , wavelet , computer science , feature extraction , feature (linguistics) , wavelet transform , data set , nuclear magnetic resonance , physics , philosophy , linguistics
If magnetic resonance spectroscopy (MRS) is to become a useful tool in clinical medicine, it will be necessary to find reliable methods for analyzing and classifying MRS data. Automated methods are desirable because they can remove user bias and can deal with large amounts of data, allowing the use of all the available information. In this study, techniques for automatically extracting features for the classification of MRS in vivo data are investigated. Among the techniques used were wavelets, principal component analysis, and linear discriminant function analysis. These techniques were tested on a set of 75 in vivo 13 C spectra of human adipose tissue from subjects from three different dietary groups (vegan, vegetarian, and omnivore). It was found that it was possible to assign automatically 94% of the vegans and omnivores to their correct dietary groups, without the need for explicit identification or measurement of peaks.