Monte Carlo generation of benign and malignant masses inside mammograms

dc.contributor.author	Λεοντίδης, Γεώργιος Κ.	el
dc.date.accessioned	2015-02-14T09:43:47Z
dc.date.available	2015-02-14T09:43:47Z
dc.date.issued	2015-02-14
dc.identifier.uri	http://hdl.handle.net/11400/6197
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://e-jst.teiath.gr/	en
dc.subject	Monte Carlo
dc.subject	Mammography
dc.subject	Simulation
dc.subject	Masses
dc.subject	Benign
dc.subject	Malignant
dc.subject	Μόντε Κάρλο
dc.subject	Μαστογραφία
dc.subject	Προσομοίωση
dc.subject	Μάζες
dc.subject	Καλοήθης
dc.subject	Κακοήθης
dc.title	Monte Carlo generation of benign and malignant masses inside mammograms	en
heal.type	journalArticle
heal.classification	Science
heal.classification	Physics
heal.classification	Επιστήμες
heal.classification	Φυσική
heal.classificationURI	http://zbw.eu/stw/descriptor/15685-2
heal.classificationURI	http://zbw.eu/stw/descriptor/15669-0
heal.classificationURI	N/A-Επιστήμες
heal.classificationURI	N/A-Φυσική
heal.language	en
heal.access	free
heal.publicationDate	2013
heal.bibliographicCitation	Leontidis, G.K. (2013). Monte Carlo generation of benign and malignant masses inside mammograms. "e-Journal of Science & Technology". [Online] 8(5): 27-32. Available from: http://e-jst.teiath.gr/	en
heal.abstract	It is a fact that breast cancer is the most common cancer among women in the western world. The most reliable radiographic tool for the detection and diagnosis of breast cancer is mammography. Interpreting a mammographic image is a difficult task and prone to many errors, thus physicists-radiologists are trained very well. Many CAD (computer-aided detection) systems have been developed in order to provide assistance to radiologists by classifying any mammographic lesions as benign or malignant but their reliability is yet to be proved. The most accurate way of spotting masses on the breast, until now, is radiologist’s opinion, making their training even more crucial. In this paper a method which simulates all the possible masses and place them on a mammography randomly using Monte Carlo technique, is proposed. After a comprehensive study of many different mammographic images, the final algorithm generates masses within a mammography, which in most cases a radiologist expert could not make the difference between a real mass and a generated one. The proposed method takes into account as many as possible variables for the generation of masses. There are definitely some improvements that could be developed in the future, mostly regarding the position of masses within the breast.	en
heal.publisher	Νερατζής, Ηλίας	el
heal.publisher	Σιανούδης, Ιωάννης	el
heal.journalName	e-Journal of Science & Technology	en
heal.journalName	e-Journal of Science & Technology	el
heal.journalType	peer-reviewed
heal.fullTextAvailability	true