Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.
Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods. The proton magnetic resonance spectroscopy data contain a total of 150 spectra (74 healthy, 23 grade II, 23 grade III, and 30 grade IV) from two databases. The datasets from both databases were first coupled together, followed by column normalization. The Kennard-Stone algorithm was used to split the datasets into its training and test sets. Performance comparison based on the overall accuracy, sensitivity, specificity, and precision was conducted. Based on the overall accuracy of our classification scheme, the dictionary pair learning method was found to outperform the sub-dictionary learning methods 97.78% compared with 68.89%, respectively. Copyright © 2016 John Wiley & Sons, Ltd.
Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences - International Campus (TUMS-IC), Tehran, Iran.|Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran, Iran.
Sikiru Afolabi
SA
Adebileje
Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran, Iran.|Department of Chemistry, Imam Khomeini International University, Tehran, Iran.
Keyvan
K
Ghasemi
Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences - International Campus, Tehran, Iran.
Hammed Tanimowo
HT
Aiyelabegan
Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences - International Campus (TUMS-IC), Tehran, Iran.|Quantitative MR Imaging and Spectroscopy Group (QMISG), Tehran, Iran.
Hamidreza
H
Saligheh Rad
D000465:Algorithms; D001932:Brain Neoplasms / Q000145:classification*; D016208:Databases, Factual; D005910:Glioma / Q000145:classification*; D006801:Humans; D000069550:Machine Learning; D066244:Proton Magnetic Resonance Spectroscopy / Q000379:methods*; D059629:Signal-To-Noise Ratio
brain gliomas; dictionary pair learning; proton magnetic resonance spectroscopy; sub-dictionary learning