Improving the classification accuracy of computer aided diagnosis through multimodality breast imaging

dc.contributor.author	Παγώνης, Νικόλαος	el
dc.contributor.author	Κάβουρας, Διονύσης Α.	el
dc.contributor.author	Σιδηρόπουλος, Κωνσταντίνος	el
dc.contributor.author	Σακελλαρόπουλος, Γεώργιος Χ.	el
dc.contributor.author	Νικηφορίδης, Γεώργιος Χ.	el
dc.date.accessioned	2015-01-30T17:37:48Z
dc.date.available	2015-01-30T17:37:48Z
dc.date.issued	2015-01-30
dc.identifier.uri	http://hdl.handle.net/11400/5164
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	Breast Cancer
dc.subject	Καρκίνος μαστού
dc.subject	Image Analysis
dc.subject	Pattern Recognition
dc.subject	Multimodality
dc.subject	US X-RAY
dc.subject	Ανάλυση εικόνας
dc.subject	Πολυτροπικότητα
dc.subject	Πρότυπα αναγνώρισης
dc.title	Improving the classification accuracy of computer aided diagnosis through multimodality breast imaging	en
heal.type	journalArticle
heal.generalDescription	Special issue: Scientific papers presented on the 3nd International Conference on Experiments/Process/System Modeling/Simulation & Optimization in Athens, 8-11 July, 2009. Mini symposium on Medical Imaging, organized by G. Panayiotakis, I. Kandarakis, G. Fountos and I. Valais	en
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.language	en
heal.access	free
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Tμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας	el
heal.publicationDate	2010
heal.bibliographicCitation	Pagonis, N., Cavouras, D.A., Sidiropoulos, K., Sakellaropoulos, G.C. and Nikiforidis, G.C. (2010). Improving the classification accuracy of computer aided diagnosis through multimodality breast imaging. "e-Journal of Science & Technology". [Online] 5(2): 33-39. Available from: http://e-jst.teiath.gr/	en
heal.abstract	The purpose of the present study is to evaluate the effect of using multiple modalities on the accuracy achieved by a computer-aided diagnosis system, designed for the detection of breast cancer. Towards this aim, 41 cases of breast cancer were selected, 18 of which were diagnosed as malignant and 23 as benign by an experienced physician. Each case included images acquired by means of two imaging modalities: x-ray and ultrasound. Manual segmentation was performed on every image in order to delineate and extract the regions of interest (ROIs) containing the breast tumors. Then 104 textural features were extracted; 52 from the x-ray images and 52 from the US images. A classification system was designed using the extracted features for every case. Firstly, features extracted from x-ray images alone were used to evaluate the system. The same task was performed for features extracted from US images alone. Lastly the combination of the two feature sets, mentioned afore, was used to evaluate the system. The proposed system that employed the Probabilistic Neural Network (PNN) classifier scored 78.05% in classification accuracy using only features from x-ray. While classification accuracy increased at 82.95% using only features from US, a significant increase in the system’s accuracy (95.12%) was achieved by using combined features from both x-ray and US. In order to minimize total training time, the proposed system adopted the Client-Server model to distribute processing tasks in a group of computers interconnected via a local area network. Depending on the number of clients employed, there was about a 4-fold reduction in training time employing 7 clients.	en
heal.publisher	Νερατζής, Ηλίας	el
heal.publisher	Σιανούδης, Ιωάννης	el
heal.publisher	Βαλαής, Ιωάννης Γ.	el
heal.publisher	Φούντος, Γεώργιος Π.	el
heal.journalName	e-Journal of Science & Technology	en
heal.journalName	e-Περιοδικό Επιστήμης & Τεχνολογίας	el
heal.journalType	peer-reviewed
heal.fullTextAvailability	true