In this section
@inproceedings{Yang:2014,
author = {Yang, G and Raschke, F and Barrick, TR and Howe, FA},
pages = {1039--1042},
publisher = {The Institute of Electrical and Electronics Engineers (IEEE)},
title = {Classification of brain tumour 1H MR spectra: Extracting features by metabolite quantification or nonlinear manifold learning?},
year = {2014}
}
TY - CPAPER
AB - © 2014 IEEE. Proton magnetic resonance spectroscopy (1H MRS) provides non-invasive information on brain tumour biochemistry. Many studies have shown that 1H MRS can be used in an objective decision support system, which gives additional diagnosis and prognostic information to the data obtained using conventional radiological modalities. Fully automatic analyses of 1H MRS have been previously applied and can be separated into two types: (i) model dependent signal quantification followed by pattern recognition (PR), or (ii) model independent PR methods. However, there is not yet a consensus as to the best techniques of MRS post-processing or feature extraction to be used for optimum classification. In this study, we analysed the single-voxel MRS acquisitions of 74 patients with histologically diagnosed brain tumours. Our classification results show that the model independent nonlinear manifold learning method can produce superior results to those of using model dependent metabolite quantification.
AU - Yang,G
AU - Raschke,F
AU - Barrick,TR
AU - Howe,FA
EP - 1042
PB - The Institute of Electrical and Electronics Engineers (IEEE)
PY - 2014///
SP - 1039
TI - Classification of brain tumour 1H MR spectra: Extracting features by metabolite quantification or nonlinear manifold learning?
ER -
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