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Automated feature extraction for the classification of human in vivo13C NMR spectra using statistical pattern recognition and wavelets
journal contribution
posted on 2023-06-08, 07:15 authored by A Rosemary Tate, Des Watson, Stephen Eglen, Theodores N Arvanitis, E Louise Thomas, Jimmy D BellIf 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.
History
Publication status
- Published
Journal
Magnetic Resonance in MedicineISSN
0740-3194Publisher
Magnetic Resonance in MedicineExternal DOI
Issue
6Volume
35Page range
834-840ISBN
0740-3194Department affiliated with
- Informatics Publications
Full text available
- No
Peer reviewed?
- Yes
Legacy Posted Date
2012-02-06Usage metrics
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