Computer-based malignancy grading of astrocytomas employing a support vector machines classifier, the WHO grading system, and the regular staining diagnostic procedure Hematoxylin-Eosin

dc.contributor.author	Γκλώτσος, Δημήτριος	el
dc.contributor.author	Σπυρίδωνος, Παναγιώτα Π.	el
dc.contributor.author	Πεταλάς, Π.	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Ραβαζούλα, Παναγιώτα	el
dc.date.accessioned	2015-05-11T08:00:29Z
dc.date.issued	2015-05-11
dc.identifier.uri	http://hdl.handle.net/11400/10109
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/15131894	en
dc.subject	Classification
dc.subject	Biopsy
dc.subject	Ταξινόμηση
dc.subject	Βιοψία
dc.title	Computer-based malignancy grading of astrocytomas employing a support vector machines classifier, the WHO grading system, and the regular staining diagnostic procedure Hematoxylin-Eosin	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.contributorName	Αραπαντώνη-Δαδιώτη, Πετρούλα	el
heal.contributorName	Λέκκα, Ι.	el
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	2004
heal.bibliographicCitation	Glotsos, D., Spyridonos, P., Petalas, P., Cavouras, D., Ravazoula, P., et al. (April 2004). Computer-based malignancy grading of astrocytomas employing a Support Vector Machines Classifier, the WHO grading system, and the regular staining diagnostic procedure Hematoxylin-Eosin. Analytical and Quantitative Cytology and Histology. 26(2). pp. 77-83. Journal of Reproductive Medicine: 2004.	en
heal.abstract	OBJECTIVE: To investigate and develop an automated technique for astrocytoma malignancy grading compatible with the clinical routine. STUDY DESIGN: One hundred forty biopsies of astrocytomas were collected from 2 hospitals. The degree of tumor malignancy was defined as low or high according to the World Health Organization grading system. From each biopsy, images were digitized and segmented to isolate nuclei from background tissue. Morphologic and textural nuclear features were quantified to encode tumor malignancy. Each case was represented by a 40-dimensional feature vector. An exhaustive search procedure in feature space was utilized to determine the best feature combination that resulted in the smallest classification error. Low and high grade tumors were discriminated using support vector machines (SVMs). To evaluate the system performance, all available data were split randomly into training and test sets. RESULTS: The best vector combination consisted of 3 textural and 2 morphologic features. Low and high grade cases were discriminated with an accuracy of 90.7% and 88.9%, respectively, using an SVM classifier with polynomial kernel of degree 2. CONCLUSION: The proposed methodology was based on standards that are common in daily clinical practice and might be used in parallel with conventional grading as a second-opinion tool to reduce subjectivity in the classification of astrocytomas.	en
heal.publisher	Journal of Reproductive Medicine	en
heal.journalName	Analytical and Quantitative Cytology and Histology	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false