Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features

dc.contributor.author	Γεωργιάδης, Παντελής	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Δασκαλάκης, Αντώνης	el
dc.contributor.author	Καγκάδης, Γεώργιος Χ.	el
dc.date.accessioned	2015-05-03T10:57:27Z
dc.date.available	2015-05-03T10:57:27Z
dc.date.issued	2015-05-03
dc.identifier.uri	http://hdl.handle.net/11400/9544
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/S0169260707002404	en
dc.subject	Brain--Tumors
dc.subject	Pattern classification
dc.subject	Εγκεφαλικός όγκος
dc.subject	Ταξινόμηση προτύπων
dc.title	Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features	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	Σολωμού, Αικατερίνη	el
heal.contributorName	Σηφάκη, Κοραλία	el
heal.contributorName	Μάλαμας, Μενέλαος	el
heal.contributorName	Νικηφορίδης, Γεώργιος Χ.	el
heal.identifier.secondary	doi:10.1016/j.cmpb.2007.10.007
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2008
heal.bibliographicCitation	Georgiadis, P., Cavouras, D., Kalatzis, I., Daskalakis, A., Kagadis, G., et al. (January 2008). Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features. Computer Methods & Programs in Biomedicine. 89(1). pp. 24-32. Elsevier Ireland Ltd: 2008. Available from: http://www.sciencedirect.com/science/article/pii/S0169260707002404 [Accessed 28/11/2007]	en
heal.abstract	The aim of the present study was to design, implement and evaluate a software system for discriminating between metastatic and primary brain tumors (gliomas and meningiomas) on MRI, employing textural features from routinely taken T1 post-contrast images. The proposed classifier is a modified probabilistic neural network (PNN), incorporating a non-linear least squares features transformation (LSFT) into the PNN classifier. Thirty-six textural features were extracted from each one of 67 T1-weighted post-contrast MR images (21 metastases, 19 meningiomas and 27 gliomas). LSFT enhanced the performance of the PNN, achieving classification accuracies of 95.24% for discriminating between metastatic and primary tumors and 93.48% for distinguishing gliomas from meningiomas. To improve the generalization of the proposed classification system, the external cross-validation method was also used, resulting in 71.43% and 81.25% accuracies in distinguishing metastatic from primary tumors and gliomas from meningiomas, respectively. LSFT improved PNN performance, increased class separability and resulted in dimensionality reduction.	en
heal.publisher	Elsevier Ireland Ltd	en
heal.journalName	Computer Methods and Programs in Biomedicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true