Assessing hip osteoarthritis severity utilizing a Probabilistic Neural Network based classification scheme

dc.contributor.author	Μπονιάτης, Ιωάννης Σ.	el
dc.contributor.author	Κωσταρίδου, Έλενα	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Καλατζής, Ιωάννης	el
dc.contributor.author	Παναγιωτόπουλος, Ηλίας	el
dc.date.accessioned	2015-04-30T11:23:40Z
dc.date.available	2015-04-30T11:23:40Z
dc.date.issued	2015-04-30
dc.identifier.uri	http://hdl.handle.net/11400/9316
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/S1350453306000634	en
dc.subject	Osteoarthritis
dc.subject	Classification--Archives
dc.subject	Οστεοαθρίτιδα
dc.subject	Ταξινόμηση
dc.title	Assessing hip osteoarthritis severity utilizing a Probabilistic Neural Network based classification scheme	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/sh85095960
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85026720
heal.contributorName	Παναγιωτάκης, Γεώργιος Σ.	el
heal.identifier.secondary	doi:10.1016/j.medengphy.2006.03.003
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2007
heal.bibliographicCitation	Boniatis, I., Costaridou, L., Cavouras, D., Kalatzis, I., Panagiotopoulos, E., et al. (March 2007). Assessing hip osteoarthritis severity utilizing a Probabilistic Neural Network based classification scheme. Medical Engineering & Physics. 29(2). pp. 227-237. Elsevier Ltd: 2007. Available from: http://www.sciencedirect.com/science/article/pii/S1350453306000634 [Accessed 19/04/2006]	en
heal.abstract	A computer-based classification system is proposed for the characterization of hips from pelvic radiographs as normal or osteoarthritic and for the discrimination among various grades of osteoarthritis (OA) severity. Pelvic radiographs of 18 patients with verified unilateral hip OA were evaluated by three experienced physicians, who assessed OA severity employing the Kellgren and Lawrence scale as: normal, mild/moderate and severe. Five run-length, 75 Laws’ and 5 novel textural features were extracted from the digitized radiographic images of each patient's osteoarthritic and contralateral normal hip joint spaces (HJSs). Each one of the three sets of textural features (run-lengths, Laws’ and novel features) was separately utilized for assigning hips into the three OA severity categories, by means of a probabilistic neural network (PNN) classifier based hierarchical tree structure. The highest classification accuracy (100%) for characterizing hips as normal, of mild/moderate or of severe OA was obtained for the novel textural features set. Additionally, the novel textural features were used to design a mathematical regression model for providing a quantitative estimation of OA severity. Measured OA severity values, as expressed by HJS-narrowing, correlated highly (r = 0.85, p < 0.001) with the predicted values by the mathematical regression model. The proposed system may be valuable in OA-patient management.	en
heal.publisher	Elsevier Ltd	en
heal.journalName	Medical Engineering & Physics	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true