Pattern-recognition system, designed on GPU, for discriminating between injured normal and pathological knee cartilage

dc.contributor.author	Κωστόπουλος, Σπυρίδων	el
dc.contributor.author	Σιδηρόπουλος, Κωνσταντίνος	el
dc.contributor.author	Γλότσος, Δημήτριος	el
dc.contributor.author	Αθανασιάδης, Εμμανουήλ Ι.	el
dc.contributor.author	Μπούτσικου, Κωνσταντίνα	el
dc.date.accessioned	2015-05-17T16:21:12Z
dc.date.available	2015-05-17T16:21:12Z
dc.date.issued	2015-05-17
dc.identifier.uri	http://hdl.handle.net/11400/10593
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/S0730725X12004286	el
dc.subject	Articular cartilage
dc.subject	Magnetic resonance imaging
dc.subject	Pattern recognition
dc.subject	Parallel processing (Electronic computers)
dc.subject	Αρθρικός χόνδρος
dc.subject	Knee injuries
dc.subject	Τραυματισμοί στο γόνατο
dc.subject	Μαγνητική τομογραφία
dc.subject	Texture analysis
dc.subject	Ανάλυση υφής
dc.subject	Αναγνώριση προτύπων
dc.subject	Graphics processor unit (GPU)
dc.subject	Μονάδα επεξεργασίας γραφικών
dc.subject	Παράλληλη επεξεργασία
dc.title	Pattern-recognition system, designed on GPU, for discriminating between injured normal and pathological knee cartilage	el
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/sh85008154
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85079741
heal.keywordURI	http://zbw.eu/stw/descriptor/15410-6
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85097826
heal.identifier.secondary	doi:10.1016/j.mri.2012.10.029
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2013-06
heal.bibliographicCitation	Kostopoulos, S., Sidiropoulos, K., Glotsos, D., Athanasiadis, E., Boutsikou, K., et.al. (2013). Pattern-recognition system, designed on GPU, for discriminating between injured normal and pathological knee cartilage. "Magnetic Resonance Imaging", 31(5), June 2013. pp. 761-770. Available from: http://www.sciencedirect.com/science/article/pii/S0730725X12004286. [Accessed 17/01/2013]	en
heal.abstract	The aim was to design a pattern-recognition (PR) system for discriminating between normal and pathological knee articular cartilage of the medial femoral (MFC) and tibial condyles (MTC). The data set comprised segmented regions of interest (ROIs) from coronal and sagittal 3-T magnetic resonance images of the MFC and MTC cartilage of young patients, 28 with abnormality-free knee and 16 with pathological findings. The PR system was designed employing the probabilistic neural network classifier, textural features from the segmented ROIs and the leave-one-out evaluation method, while the PR system's precision to “unseen” data was assessed by employing the external cross-validation method. Optimal system design was accomplished on a consumer graphics processing unit (GPU) using Compute Unified Device Architecture parallel programming. PR system design on the GPU required about 3.5 min against 15 h on a CPU-based system. Highest classification accuracies for the MFC and MTC cartilages were 93.2% and 95.5%, and accuracies to “unseen” data were 89% and 86%, respectively. The proposed PR system is housed in a PC, equipped with a consumer GPU, and it may be easily retrained when new verified data are incorporated in its repository and may be of value as a second-opinion tool in a clinical environment.	en
heal.journalName	Magnetic Resonance Imaging	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true