Automated feature extraction for the classification of human in vivo 13C NMR spectra using statistical pattern recognition and wavelets.
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 13C 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.
Tate
A R
AR
School of Cognitive and Computing Science, University of Sussex, Falmer, Brighton, United Kingdom.
Watson
D
D
Eglen
S
S
Arvanitis
T N
TN
Thomas
E L
EL
Bell
J D
JD
D000273:Adipose Tissue; D004032:Diet; D014676:Diet, Vegetarian; D005260:Female; D006801:Humans; D009682:Magnetic Resonance Spectroscopy / Q000379:methods; D008297:Male; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted