Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns

dc.contributor.author	Ιακωβίδης, Δημήτρης Κ.	el
dc.contributor.author	Κεραμυδάς, Ευστράτιος Γ.	el
dc.contributor.author	Μαρούλης, Δημήτρης Ε.	el
dc.date.accessioned	2015-06-07T05:26:04Z
dc.date.available	2015-06-07T05:26:04Z
dc.date.issued	2015-06-07
dc.identifier.uri	http://hdl.handle.net/11400/15376
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/S0933365710000369	en
dc.subject	Thyroid nodules
dc.subject	Θυρεοειδής αδένες
dc.subject	Ultrasound imaging
dc.subject	Υπέρηχη απεικόνιση
dc.title	Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns	en
heal.type	journalArticle
heal.generalDescription	Knowledge Discovery and Computer-Based Decision Support in Biomedicine	en
heal.classification	Technology
heal.classification	Electronics
heal.classification	Τεχνολογία
heal.classification	Ηλεκτρονική
heal.classificationURI	http://zbw.eu/stw/descriptor/10470-6
heal.classificationURI	http://zbw.eu/stw/descriptor/10455-2
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Ηλεκτρονική
heal.identifier.secondary	doi:10.1016/j.artmed.2010.04.004
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Ηλεκτρονικών Μηχανικών Τ.Ε.	el
heal.publicationDate	2010-09
heal.bibliographicCitation	Iakovidis, D., Keramidas, E. and Maroulis, D. (September 2010). Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns. Artificial Intelligence in Medicine. 50(1). pp. 33-41. Elsevier B.V: 2010. Available from: http://www.sciencedirect.com/science/article/pii/S0933365710000369 [Accessed 27/04/2010]	en
heal.abstract	Objective This paper proposes a novel approach for thyroid ultrasound pattern representation. Considering that texture and echogenicity are correlated with thyroid malignancy, the proposed approach encodes these sonographic features via a noise-resistant representation. This representation is suitable for the discrimination of nodules of high malignancy risk from normal thyroid parenchyma. Materials and methods The material used in this study includes a total of 250 thyroid ultrasound patterns obtained from 75 patients in Greece. The patterns are represented by fused vectors of fuzzy features. Ultrasound texture is represented by fuzzy local binary patterns, whereas echogenicity is represented by fuzzy intensity histograms. The encoded thyroid ultrasound patterns are discriminated by support vector classifiers. Results The proposed approach was comprehensively evaluated using receiver operating characteristics (ROCs). The results show that the proposed fusion scheme outperforms previous thyroid ultrasound pattern representation methods proposed in the literature. The best classification accuracy was obtained with a polynomial kernel support vector machine, and reached 97.5% as estimated by the area under the ROC curve. Conclusions The fusion of fuzzy local binary patterns and fuzzy grey-level histogram features is more effective than the state of the art approaches for the representation of thyroid ultrasound patterns and can be effectively utilized for the detection of nodules of high malignancy risk in the context of an intelligent medical system.	en
heal.publisher	Elsevier B.V.	en
heal.journalName	Artificial Intelligence in Medicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true