Automatic segmentation of routinely H&E stained microscopic images by combining support vector machine clustering and active contour models

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	2015-05-11T08:24:44Z
dc.date.issued	2015-05-11
dc.identifier.uri	http://hdl.handle.net/11400/10111
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.pubfacts.com/detail/15678615/Automated-segmentation-of-routinely-hematoxylin-eosin-stained-microscopic-images-by-combining-suppor	en
dc.subject	Biopsy
dc.subject	Automation
dc.subject	Βιοψία
dc.subject	Αυτοματοποίηση
dc.title	Automatic segmentation of routinely H&E stained microscopic images by combining support vector machine clustering and active contour models	en
heal.type	journalArticle
heal.classification	Technology
heal.classification	Biomedical engineering
heal.classification	Τεχνολογία
heal.classification	Βιοϊατρική τεχνολογία
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85014237
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Βιοϊατρική τεχνολογία
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., Cavouras, D., Ravazoula, I., Dadioti, P., et al. (December 2004). Automatic segmentation of routinely H&E stained microscopic images by combining support vector machine clustering and active contour models. Analytical and Quantitative Cytology and Histology. 26(6). pp. 331-341. Journal of Reproductive Medicine: 2004.	en
heal.abstract	To develop a method for the automated segmentation of images of routinely hematoxylin-eosin (H-E)-stained microscopic sections to guarantee correct results in computer-assisted microscopy. Clinical material was composed 50 H-E-stained biopsies of astrocytomas and 50 H-E-stained biopsies of urinary bladder cancer. The basic idea was to use a support vector machine clustering (SVMC) algorithm to provide gross segmentation of regions holding nuclei and subsequently to refine nuclear boundary detection with active contours. The initialization coordinates of the active contour model were defined using a SVMC pixel-based classification algorithm that discriminated nuclear regions from the surrounding tissue. Starting from the boundaries of these regions, the snake fired and propagated until converging to nuclear boundaries. The method was validated for 2 different types of H-E-stained images. Results were evaluated by 2 histopathologists. On average, 94% of nuclei were correctly delineated. The proposed algorithm could be of value in computer-based systems for automated interpretation of microscopic images.	en
heal.publisher	Journal of Reproductive Medicine	en
heal.journalName	Analytical and Quantitative Cytology and Histology	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false