A pattern recognition system for brain tumour grade prediction based on histopathological material and features extracted at different optical magnifications

dc.contributor.author	Κωνσταντίνου, Χρήστος	el
dc.contributor.author	Μανέας, Ευθύμιος	el
dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Ραβαζούλα, Παναγιώτα	el
dc.date.accessioned	2015-02-03T18:23:55Z
dc.date.available	2015-02-03T18:23:55Z
dc.date.issued	2015-02-03
dc.identifier.uri	http://hdl.handle.net/11400/5591
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://e-jst.teiath.gr/	en
dc.subject	Astrocytomas
dc.subject	Αστροκυτώματα
dc.subject	Brain cancer
dc.subject	Καρκίνο του εγκεφάλου
dc.subject	Biopsy
dc.subject	Βιοψία
dc.subject	Grade
dc.subject	Βαθμός
dc.subject	Diagnosis
dc.subject	Διάγνωση
dc.subject	Pattern recognition
dc.subject	Αναγνώριση προτύπων
dc.title	A pattern recognition system for brain tumour grade prediction based on histopathological material and features extracted at different optical magnifications	en
heal.type	journalArticle
heal.generalDescription	Special issue: Workshop on Bio-Medical Instrumentation and related Engineering And Physical Sciences, Technological Educational Institute of Athens, 6 July 2012	en
heal.classification	Medicine
heal.classification	Internal medicine
heal.classification	Ιατρική
heal.classification	Εσωτερική παθολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85067347
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Εσωτερική παθολογία
heal.contributorName	Κάβουρας, Διονύσης Α.	el
heal.language	en
heal.access	free
heal.publicationDate	2012
heal.bibliographicCitation	Konstandinou, C., Maneas, E., Glotsos, D., Kostopoulos, S., Ravazoula, P., et al. (2012). A pattern recognition system for brain tumour grade prediction based on histopathological material and features extracted at different optical magnifications. "e-Journal of Science & Technology". [Online] 7(3): 53-59. Available from: http://e-jst.teiath.gr/	en
heal.abstract	The purpose of this study is to develop a computer-assisted diagnosis system for improving diagnostic accuracy in brain cancer classification into grades of malignancy. The clinical material comprised biopsies of patients with confirmed brain cancer. Images were digitized from the original material using a digital light microscopy imaging system (LEICA Axiostar plus coupled with a LEICA DFC 420C camera, Leica Microsystems GmbH). The digitized images were processed for the separation of nuclei from the surrounding tissue using edge detection techniques. Then, features were extracted from segmented nuclei at different optical magnifications to describe each sample-patient malignancy status. Moreover, samples were examined by an expert pathologist (P.R.), who assessed qualitative a number of crucial histological characteristics that are used by the World Health Organization as criteria for tumours’ grading. These features comprised the input to a pattern recognition system, which was designed in order to predict the risks of malignancy of each tumor. The system was structured using the Probabilistic Neural Network (PNN) and Support Vector Machine (SVM) classifier alternatively. Using the leave-one-out method, the PNN resulted in 94.4% accuracy, while the SVM showed 96.3%. To assess the generalization of the system to unknown data, the external cross validation was used and gave 77.8% prediction for both classifiers. Results show that computer-assisted diagnosis offers a valuable tool providing second opinion consultancy to expert physicians, which contributes towards a better and more accurate diagnostic conclusion.	en
heal.publisher	Νερατζής, Ηλίας	el
heal.publisher	Σιανούδης, Ιωάννης	el
heal.publisher	Βαλαής, Ιωάννης Γ.	el
heal.publisher	Φούντος, Γεώργιος Π.	el
heal.journalName	e-Journal of Science & Technology	en
heal.journalName	e-Περιοδικό Επιστήμης & Τεχνολογίας	el
heal.journalType	peer-reviewed
heal.fullTextAvailability	true