Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound

dc.contributor.author	Πήλιουρας, Νικόλαος	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Δημητρόπουλος, Νικόλαος	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.date.accessioned	2015-04-29T09:53:36Z
dc.date.available	2015-04-29T09:53:36Z
dc.date.issued	2015-04-29
dc.identifier.uri	http://hdl.handle.net/11400/9240
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.sciencedirect.com/science/article/pii/S0895611104000515	en
dc.subject	Breast ultrasound
dc.subject	Breast lesion discrimination
dc.subject	Υπερηχογράφημα μαστού
dc.subject	Διάκριση της βλάβης του μαστού
dc.title	Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound	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.identifier.secondary	doi:10.1016/j.compmedimag.2004.04.003
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2004
heal.bibliographicCitation	Piliouras, N., Kalatzis, I., Dimitropoulos, N. and Cavouras, D. (July 2004). Development of the cubic least squares mapping linear-kernel support vector machine classifier for improving the characterization of breast lesions on ultrasound. Computerized Medical Imaging and Graphics. 28(5). pp. 247-255. Elsevier Ltd: 2004. Available from: http://www.sciencedirect.com/science/article/pii/S0895611104000515 [Accessed 22/06/2004]	en
heal.abstract	An efficient classification algorithm is proposed for characterizing breast lesions. The algorithm is based on the cubic least squares mapping and the linear-kernel support vector machine (SVMLSM) classifier. Ultrasound images of 154 confirmed lesions (59 benign and 52 malignant solid masses, 7 simple cysts, and 32 complicated cysts) were manually segmented by a physician using a custom developed software. Texture and outline features and the SVMLSM algorithm were used to design a hierarchical tree classification system. Classification accuracy was 98.7%, misdiagnosing 1 malignant an 1 benign solid lesions only. This system may be used as a second opinion tool to the radiologists.	en
heal.publisher	Elsevier Ltd	en
heal.journalName	Computerized Medical Imaging and Graphics	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true