Classification of histological images of the endometrium using texture features

dc.contributor.author	Βλαχοκώστα, Αλεξάνδρα Α.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.contributor.author	Κόνδη-Παφίτη, Αγάθη	el
dc.contributor.author	Βλάχος, Νίκος	el
dc.date.accessioned	2015-02-09T13:08:11Z
dc.date.issued	2015-02-09
dc.identifier.uri	http://hdl.handle.net/11400/5927
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.subject	Neural networks
dc.subject	Endometrium
dc.subject	Νευρωνικό δίκτυο
dc.subject	Ενδομήτριο
dc.title	Classification of histological images of the endometrium using texture 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://lod.nal.usda.gov/12606
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85043080
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	2013
heal.bibliographicCitation	Vlachokosta, A., Asvestas, P., Matsopoulos, G., Kondi-Pafiti, A. and Vlachos, N., (2013). Classification of histological images of the endometrium using texture features. Analytical and Quantitative Cytopathology and Histopathology. 35(2). Science Printers and Publishers Inc: 2013.	en
heal.abstract	OBJECTIVE: To present a texture analysis method in order to achieve texture classification for 240 histological images of the endometrium. STUDY DESIGN: A total of 128 patients with endometrial cancer and 112 subjects with no pathological condition were imaged. For each image 190 texture features were initially extracted, derived from the wavelets, the Gabor filters, and the Law's masks, which were reduced after feature selection in only 4 features. RESULTS: The images were classified into 2 categories using artificial neural networks, and the reported classification accuracy was 98.1%. CONCLUSION: The results showed that there was a strong discrimination between histological images of cancerous and normal tissue of the endometrium, based on the proposed set of texture features.	en
heal.publisher	Science Printers and Publishers Inc	en
heal.journalName	Analytical and Quantitative Cytopathology and Histopathology	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true