Classification of hysteroscopical images using texture and vessel descriptors

dc.contributor.author	Βλαχοκώστα, Αλεξάνδρα Α.	el
dc.contributor.author	Ασβεστάς, Παντελής Α.	el
dc.contributor.author	Γκρόζου, Φανή	el
dc.contributor.author	Λαβασίδης, Λάζαρος	el
dc.contributor.author	Ματσόπουλος, Γεώργιος Κ.	el
dc.date.accessioned	2015-02-09T12:47:43Z
dc.date.available	2015-02-09T12:47:43Z
dc.date.issued	2015-02-09
dc.identifier.uri	http://hdl.handle.net/11400/5925
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	Hysteroscopy
dc.subject	Endometrium
dc.subject	Υστεροσκόπηση
dc.subject	Ενδομήτριο
dc.title	Classification of hysteroscopical images using texture and vessel descriptors	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://id.loc.gov/authorities/subjects/sh85063859
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85043080
heal.contributorName	Πασχόπουλος, Μηνάς	el
heal.identifier.secondary	DOI 10.1007/s11517-013-1058-1
heal.language	en
heal.access	campus
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., Gkrozou, F., Lavasidis, L., Matsopoulos, G., et al. (2013). Classification of hysteroscopical images using texture and vessel descriptors. Medical & Biological Engineering & Computing. vol. 51. pp. 859–867. Springer-Verlag: 2013. Available from: http://download.springer.com [Accessed 18/03/2013]	en
heal.abstract	In recent years, hysteroscopy, used as an outpatient office procedure, in combination with endometrial biopsy, has demonstrated its great potential as the method of first choice in the diagnosis of various gynecological abnormalities including abnormal uterine bleeding (AUB) and endometrial cancer (CA). In patients suffering with AUB, the blood vessels of the endometrium are hypertrophic, whereas in the case of CA vascularization is irregular or anarchic. In this paper, a methodology for the classification of hysteroscopical images of endometrium using vessel and texture features is presented. A total of 28 patients with abnormal uterine bleeding, 10 patients with endometrial cancer and 39 subjects with no pathological condition were imaged. 16 of the patients with AUB were premenopausal and 12 postmenopausal, all with CA were postmenopausal, and all with no pathological condition were premenopausal. All images were examined for the appearance of endometrial vessels and non-vascular structures. For each image, 167 texture and vessel’s features were initially extracted, which were reduced after feature selection in only 4 features. The images were classified into three categories using artificial neural networks and the reported classification accuracy was 91.2 %, while the specificity and sensitivity were 83.8 and 93.6 % respectively.	en
heal.publisher	Springer-Verlag	en
heal.journalName	Medical & Biological Engineering & Computing	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true