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Pattern Recognition Analysis of MR Spectra

Ortega-Martorell, S and Julià-Sapé, M and Lisboa, P and Arús, C (2016) Pattern Recognition Analysis of MR Spectra. In: Griffiths, J and Bottomley, P, (eds.) Handbook of in vivo Magnetic Resonance Spectroscopy. John Wiley & Sons. ISBN 978-1-118-99766-6

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Abstract

The need for multivariate analysis of magnetic resonance spectroscopy (MRS) data was recognized about 20 years ago, when it became evident that spectral patterns were characteristic of some diseases. Despite this, there is no generally accepted methodology for performing pattern recognition (PR) analysis of MRS data sets. Here, the data acquisition and processing requirements for performing successful PR as applied to human MRS studies are introduced, and the main techniques for feature selection, extraction, and classification are described. These include methods of dimensionality reduction such as principal component analysis (PCA), independent component analysis (ICA), non-negative matrix factorization (NMF), and feature selection. Supervised methods such as linear discriminant analysis (LDA), logistic regression (LogR), and nonlinear classification are discussed separately from unsupervised and semisupervised classification techniques, including k –means clustering. Methods for testing and metrics for gauging the performance of PR models (sensitivity and specificity, the ‘Confusion Matrix’, ‘k –fold cross-validation’, ‘Leave One Out’, ‘Bootstrapping’, the ‘Receiver Operating Characteristic curve’, and balanced error and accuracy rates) are briefly described. This article ends with a summary of the main lessons learned from PR applied to MRS to date.

Item Type: Book Section
Subjects: Q Science > QA Mathematics
R Medicine > R Medicine (General)
Divisions: Applied Mathematics
Publisher: John Wiley & Sons
Date Deposited: 15 Feb 2017 09:53
Last Modified: 15 Feb 2017 09:53
Editors: Griffiths, J and Bottomley, P
URI: http://researchonline.ljmu.ac.uk/id/eprint/5463

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