Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra
Copyright © 2016 John Wiley & Sons, Ltd.
| Publié dans: | Magnetic resonance in chemistry : MRC. - 1985. - 55(2017), 4 vom: 22. Apr., Seite 318-322 |
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| Auteur principal: | |
| Autres auteurs: | , , |
| Format: | Article en ligne |
| Langue: | English |
| Publié: |
2017
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| Accès à la collection: | Magnetic resonance in chemistry : MRC |
| Sujets: | Journal Article brain gliomas dictionary pair learning proton magnetic resonance spectroscopy sub-dictionary learning |
| Résumé: | Copyright © 2016 John Wiley & Sons, Ltd. 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 |
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| Description: | Date Completed 05.04.2018 Date Revised 05.04.2018 published: Print-Electronic Citation Status MEDLINE |
| ISSN: | 1097-458X |
| DOI: | 10.1002/mrc.4532 |