Abstract #1964
One-Class Classifier for Accurate Brain Tissue Classification from Noisy 1H-MRS Spectra
Keyvan Ghassemi 1,2 , Mohammadreza Khanmohammadi Khorami 1 , and Hamidreza Saligheh Rad 2,3
1
Chemistry Department, Faculty of Science,
Imam Khomeini International University, Qazvin, Iran,
2
Quantitative
MR Imaging and Spectroscopy Group, Research Center for
Molecular and Cellular Imaging, Tehran University of
Medical Sciences, Tehran, Iran,
3
Department
of Medical Physics and Biomedical Engineering, School of
Medicine, Tehran University of Medical Sciences, Tehran,
Iran
Low signal to noise ratio (SNR), baseline distortions,
large line-widths and asymmetric line-shapes caused by
poor shimming, as well as contaminations caused by
significant chemical shift displacement effects produce
complicated MRS signals. Totally 139 spectra from
healthy and tomure glial brains 10 healthy cases,11
grade II, 6 grade III, as well as 9 grade IV brain
gliomas were collected. SIMCA was used by application of
PCA in common rule and by using of the NMF. Results of
robust SIMCA showed significant modification in
percentage of correct classified samples after
application of NMF for better decomposition of noisy
measurements.
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