Breast cancer characterization based on image classification of tissue sections visualized under low magnification

dc.contributor.author	Λουκάς, Κωνσταντίνος	el
dc.contributor.author	Κωστόπουλος, Σπύρος	el
dc.contributor.author	Τανογλίδη, Άννα	el
dc.contributor.author	Γκλώτσος, Δημήτρης	el
dc.contributor.author	Σφήκας, Κωνσταντίνος	el
dc.date.accessioned	2015-06-06T16:53:49Z
dc.date.available	2015-06-06T16:53:49Z
dc.date.issued	2015-06-06
dc.identifier.uri	http://hdl.handle.net/11400/15340
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.hindawi.com	en
dc.subject	Καρκίνος του μαστού
dc.title	Breast cancer characterization based on image classification of tissue sections visualized under low magnification	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://skos.um.es/unesco6/320713
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ογκολογία
heal.contributorName	Κάβουρας, Διονύσιος	el
heal.identifier.secondary	DOI: 10.1155/2013/829461
heal.language	en
heal.access	campus
heal.publicationDate	2013
heal.bibliographicCitation	Loukas, C., Kostopoulos, S., Tanoglidi, A., Glotsos, D., Sfikas, C. et al. (2013) Breast cancer characterization based on image classification of tissue sections visualized under low magnification. "Computational and Mathematical Methods in Medicine", 2013	en
heal.abstract	Rapid assessment of tissue biopsies is a critical issue in modern histopathology. For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications, respectively. In this study, we focus on the development of a pattern classification system for the assessment of breast cancer images captured under low magnification (×10). Sixty-five regions of interest were selected from 60 images of breast cancer tissue sections. Texture analysis provided 30 textural features per image. Three different pattern recognition algorithms were employed (kNN, SVM, and PNN) for classifying the images into three malignancy grades: I-III. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes. The average discrimination efficiency of the kNN, SVM, and PNN classifiers in the training mode was close to 97%, 95%, and 97%, respectively, whereas in the test mode, the average classification accuracy achieved was 86%, 85%, and 90%, respectively. Assessment of breast cancer tissue sections could be applied in complex large-scale images using textural features and pattern classifiers. The proposed technique provides several benefits, such as speed of analysis and automation, and could potentially replace the laborious task of visual examination.	en
heal.journalName	Computational and Mathematical Methods in Medicine	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true