Computer-based image analysis system designed to differentiate between low-grade and high-grade laryngeal cancer cases

dc.contributor.author	Νίνος, Κωνσταντίνος	el
dc.contributor.author	Κωστόπουλος, Σπύρος	el
dc.contributor.author	Σιδηρόπουλος, Κωνσταντίνος	el
dc.contributor.author	Καλαϊτζής, Ιωάννης	el
dc.contributor.author	Γκλώτσος, Δημήτρης	el
dc.date.accessioned	2015-06-10T22:39:04Z
dc.date.issued	2015-06-11
dc.identifier.uri	http://hdl.handle.net/11400/15663
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.aqch.com/	en
dc.subject	Καρκίνος του λάρυγγα
dc.subject	Ιστοπαθολογική εξέταση
dc.subject	Histopathology
dc.title	Computer-based image analysis system designed to differentiate between low-grade and high-grade laryngeal cancer cases	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Oncology
heal.classification	Ιατρική
heal.classification	Ογκολογία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85094724
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ογκολογία
heal.contributorName	Αθανασιάδης, Εμμανουήλ	el
heal.contributorName	Ραβαζούλα, Παναγιώτα	el
heal.contributorName	Παναγιωτάκης, Γιώργος	el
heal.contributorName	Οικονόμου, Γιώργος	el
heal.contributorName	Κάβουρας, Διονύσης	el
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.publicationDate	2013
heal.bibliographicCitation	Ninos, K., Kostopoulos, S., Sidiropoulos, K., Kalatzis, I., Glotsos, D. et al. (2013) Computer-based image analysis system designed to differentiate between low-grade and high-grade laryngeal cancer cases. "Analytical and Quantitative Cytology and Histology", 35 (5), p.261-272	en
heal.abstract	To design a pattern recognition (PR) system for discriminating between low- and high-grade laryngeal cancer cases, employing immunohistochemically stained, for p63 expression, histopathology images. STUDY DESIGN: The PR system was designed to assist in the physician's diagnosis for improving patient survival. The material comprised 55 verified cases of laryngeal cancer, 21 of low-grade and 34 of high-grade malignancy. Histopathology images were first processed for automatically segmenting p63 expressed nuclei. Fiftytwo features were next extracted from the segmented nuclei, concerning nuclei texture, shape, and physical topology in the image. Those features and the Probabilistic Neural Network classifier were used to design the PR system on the multiprocessors of the Nvidia 580 GTX graphics processing unit (GPU) card using the Compute Unified Device Architecture parallel programming model and C++ programming language. RESULTS: PR system performance in classifying laryngeal cancer cases as low grade and high grade was 85.7% and 94.1%, respectively. The system's overall accuracy was 90.9%, using 7 features, and its estimated accuracy to "unseen" by the system cases was 80%. CONCLUSION: Optimum system design was feasible after employing parallel processing techniques and GPU technology. The proposed system was structured so as to function in a clinical environment, as a research tool, and with the capability of being redesigned on site when new verified cases are added to its repository. (Anal Quant Cytopathol Histopathol 2013;35:261-272).	en
heal.journalName	Analytical and Quantitative Cytology and Histology	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	false