Abstract #3368
Supervised Non-Negative Matrix Factorization Based Classification of Multiparametric MR Imaging of Gliomas at 3T
Fusun Citak Er 1 , Zeynep Firat 2 , Basar Sarikaya 2 , Ugur Ture 3 , and Esin Ozturk-Isik 4
1
Department of Genetics and Bioengineering,
Yeditepe University, Istanbul, Turkey,
2
Department
of Radiology, Yeditepe University Hospital, Istanbul,
Turkey,
3
Department
of Neurosurgery, Yeditepe University Hospital, Istanbul,
Turkey,
4
Department
of Biomedical Engineering, Yeditepe University,
Istanbul, Turkey
This study aims to evaluate the performance of
non-negative matrix factorization (NMF) for supervised
classification of brain tumor grade using quantitative
multiparametric MR imaging at 3T. Fractional anisotropy,
cerebral blood volume, mean transit time, cerebral blood
flow, apparent diffusion coefficient and peak height
ratios of N-acetyl aspartate over creatine (NAA/Cr) and
choline over creatine (Cho/Cr) of thirty newly diagnosed
glioma patients were calculated, and used as predictors
for classification of tumor grade. NMF results were
compared with k-nearest neighbor (kNN) algorithm. This
study showed that non-negative matrix factorization
performed better than kNN in glioma grading using
multiparametric MRI at 3T.
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