Image analysis methods for solitary pulmonary nodule characterization by computed tomography

dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Πρασόπουλος, Παναγιώτης	el
dc.contributor.author	Παντελίδης, Νικόλαος	el
dc.date.accessioned	2015-06-13T11:32:00Z
dc.date.available	2015-06-13T11:32:00Z
dc.date.issued	2015-06-13
dc.identifier.uri	http://hdl.handle.net/11400/15878
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.elsevier.com/	en
dc.subject	Image analysis
dc.subject	Image processing
dc.subject	Thorax solitary pulmonary nodule
dc.subject	Thorax image analysis
dc.subject	Ανάλυση εικόνας
dc.subject	Επεξεργασία εικόνας
dc.subject	μονήρης πνευμονικός όζος θώρακος
dc.subject	Ανάλυση εικόνας θώρακος
dc.title	Image analysis methods for solitary pulmonary nodule characterization by computed tomography	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Medical physics
heal.classification	Ιατρική
heal.classification	Ιατρική φυσική
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85083001
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ιατρική φυσική
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh98002813
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85064446
heal.identifier.secondary	doi:10.1016/0720-048X(92)90079-O
heal.language	en
heal.access	campus
heal.publicationDate	1992-05
heal.bibliographicCitation	CAVOURAS, D.A., PRASSOPOULOS, P. & PANTELIDIS, N. (1992). Image analysis methods for solitary pulmonary nodule characterization by computed tomography. European Journal of Radiology. [online] 14 (3). p. 169-172. Available from: http://www.elsevier.com/[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	en
heal.journalName	European Journal of Radiology	en
heal.journalType	non peer-reviewed
heal.fullTextAvailability	true