Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods

dc.contributor.author	Γεωργιάδης, Παντελής	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Αθανασιάδης, Εμμανουήλ	el
dc.date.accessioned	2015-05-03T11:18:41Z
dc.date.available	2015-05-03T11:18:41Z
dc.date.issued	2015-05-03
dc.identifier.uri	http://hdl.handle.net/11400/9550
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.sciencedirect.com/science/article/pii/S0730725X0800177X	en
dc.subject	Brain--Tumors
dc.subject	Pattern classification
dc.subject	Όγκοι εγκεφάλου
dc.subject	Ταξινόμηση προτύπων
dc.title	Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Biomedical engineering
heal.classification	Ιατρική
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85016351
heal.contributorName	Κωστόπουλος, Σπυρίδων	el
heal.contributorName	Σηφάκη, Κοραλία	el
heal.contributorName	Μάλαμας, Μενέλαος	el
heal.contributorName	Νικηφορίδης, Γεώργιος Χ.	el
heal.contributorName	Σολωμού, Αικατερίνη	el
heal.identifier.secondary	doi:10.1016/j.mri.2008.05.017
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2009
heal.bibliographicCitation	Georgiadis, P., Cavouras, D., Kalatzis, I., Glotsos, D., Athanasiadis, E., et al. (January 2009). Enhancing the discrimination accuracy between metastases, gliomas and meningiomas on brain MRI by volumetric textural features and ensemble pattern recognition methods. Magnetic Resonance Imaging. 27(1). pp. 120-130. Elsevier Inc: 2009. Available from: http://www.sciencedirect.com/science/article/pii/S0730725X0800177X [Accessed 07/07/2008]	en
heal.abstract	Three-dimensional (3D) texture analysis of volumetric brain magnetic resonance (MR) images has been identified as an important indicator for discriminating among different brain pathologies. The purpose of this study was to evaluate the efficiency of 3D textural features using a pattern recognition system in the task of discriminating benign, malignant and metastatic brain tissues on T1 postcontrast MR imaging (MRI) series. The dataset consisted of 67 brain MRI series obtained from patients with verified and untreated intracranial tumors. The pattern recognition system was designed as an ensemble classification scheme employing a support vector machine classifier, specially modified in order to integrate the least squares features transformation logic in its kernel function. The latter, in conjunction with using 3D textural features, enabled boosting up the performance of the system in discriminating metastatic, malignant and benign brain tumors with 77.14%, 89.19% and 93.33% accuracy, respectively. The method was evaluated using an external cross-validation process; thus, results might be considered indicative of the generalization performance of the system to “unseen” cases. The proposed system might be used as an assisting tool for brain tumor characterization on volumetric MRI series.	en
heal.publisher	Elsevier Inc	en
heal.journalName	Magnetic Resonance Imaging	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true