An image-analysis system based on support vector machines for automatic grade diagnosis of brain-tumour astrocytomas in clinical routine

dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Ραβαζούλα, Παναγιώτα	el
dc.contributor.author	Αραπαντώνη-Δαδιώτη, Πετρούλα	el
dc.date.accessioned	2015-05-11T08:37:18Z
dc.date.issued	2015-05-11
dc.identifier.uri	http://hdl.handle.net/11400/10113
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.ncbi.nlm.nih.gov/pubmed/16403707	en
dc.subject	Brain tumour
dc.subject	Biopsy
dc.subject	Εγκεφαλικός όγκος
dc.subject	Βιοψία
dc.title	An image-analysis system based on support vector machines for automatic grade diagnosis of brain-tumour astrocytomas in clinical routine	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Oncology
heal.classification	Ιατρική
heal.classification	Ογκολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85094724
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ογκολογία
heal.contributorName	Νικηφορίδης, Γεώργιος Χ.	el
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2005
heal.bibliographicCitation	Glotsos, D., Spyridonos, P., Cavouras, D., Ravazoula, P., Dadioti, P., et al. (September 2005). An image analysis system based on support vector machines for automatic grade diagnosis of brain tumour astrocytomas in clinical routine. Medical Informatics & The Internet in Medicine. 3093). pp. 179-193. Taylor & Francis: 2005.	en
heal.abstract	An image-analysis system based on the concept of Support Vector Machines (SVM) was developed to assist in grade diagnosis of brain tumour astrocytomas in clinical routine. One hundred and forty biopsies of astrocytomas were characterized according to the WHO system as grade II, III and IV. Images from biopsies were digitized, and cell nuclei regions were automatically detected by encoding texture variations in a set of wavelet, autocorrelation and parzen estimated descriptors and using an unsupervised SVM clustering methodology. Based on morphological and textural nuclear features, a decision-tree classification scheme distinguished between different grades of tumours employing an SVM classifier. The system was validated for clinical material collected from two different hospitals. On average, the SVM clustering algorithm correctly identified and accurately delineated 95% of all nuclei. Low-grade tumours were distinguished from high-grade tumours with an accuracy of 90.2% and grade III from grade IV with an accuracy of 88.3% The system was tested in a new clinical data set, and the classification rates were 87.5 and 83.8%, respectively. Segmentation and classification results are very encouraging, considering that the method was developed based on every-day clinical standards. The proposed methodology might be used in parallel with conventional grading to support the regular diagnostic procedure and reduce subjectivity in astrocytomas grading.	en
heal.publisher	Taylor & Francis	en
heal.journalName	Medical Informatics & The Internet in Medicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false