Image analysis methods for solitary pulmonary nodule characterization by computed tomography

dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Πρασόπουλος, Παναγιώτης Π.	el
dc.contributor.author	Παντελίδης, Ν.	el
dc.date.accessioned	2015-04-29T07:44:27Z
dc.date.available	2015-04-29T07:44:27Z
dc.date.issued	2015-04-29
dc.identifier.uri	http://hdl.handle.net/11400/9227
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/0720048X9290079O	en
dc.subject	Thorax image analysis
dc.subject	Image analysis
dc.subject	Ανάλυση θωρακικής εικόνας
dc.subject	Ανάλυση εικόνας
dc.title	Image analysis methods for solitary pulmonary nodule characterization by computed tomography	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/sh98002813
heal.identifier.secondary	doi:10.1016/0720-048X(92)90079-O
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	1992
heal.bibliographicCitation	Cavouras, D., Prassopoulos, P. and Pantelidis, N. (May–June 1992). Image analysis methods for solitary pulmonary nodule characterization by computed tomography. European Journal of Radiology. 14(3). pp. 169-172. Elsevier Ireland Ltd: 1992. Available from: http://www.sciencedirect.com/science/article/pii/0720048X9290079O [Accessed 23/03/2004]	en
heal.abstract	Computer software was designed for classifying solitary pulmonary nodules (SPNs) into benign and malignant from their CT images, using image analysis methods. The system made use of three features, computed from the CT density matrix of the SPN, and a class-discriminating algorithm. System evaluation was performed on 51 histologically confirmed SPNs of indeterminate CT diagnosis. Overall classification accuracy in distinguishing benign and malignant SPNs was 90.2%, while 83.3% of the benign and 93.9% of the malignant SPNs were correctly classified. The proposed system may be of value to the radiologist in assessing the probability of malignancy in patients with a solitary pulmonary nodule.	en
heal.publisher	Elsevier Ireland Ltd.	en
heal.journalName	European Journal of Radiology	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true