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dc.creatorQuintana Zurro, Clara Ines-
dc.creatorRedondo, Marcelo-
dc.creatorTirao, German Alfredo-
dc.date2017-12-27T19:26:14Z-
dc.date2017-12-27T19:26:14Z-
dc.date2014-02-
dc.date2017-12-26T20:40:12Z-
dc.date.accessioned2019-04-29T15:46:04Z-
dc.date.available2019-04-29T15:46:04Z-
dc.date.issued2014-02-
dc.identifierTirao, German Alfredo; Redondo, Marcelo; Quintana Zurro, Clara Ines; Implementation of several mathematical algorithms to breast tissue density classification; Pergamon-Elsevier Science Ltd.; Radiation Physics and Chemistry (Oxford); 95; 2-2014; 261-263-
dc.identifier0969-806X-
dc.identifierhttp://hdl.handle.net/11336/31701-
dc.identifierCONICET Digital-
dc.identifierCONICET-
dc.identifier.urihttp://rodna.bn.gov.ar:8080/jspui/handle/bnmm/301510-
dc.descriptionThe accuracy of mammographic abnormality detection methods is strongly dependent on breast tissue characteristics, where a dense breast tissue can hide lesions causing cancer to be detected at later stages. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. This paper presents the implementation and the performance of different mathematical algorithms designed to standardize the categorization of mammographic images, according to the American College of Radiology classifications. These mathematical techniques are based on intrinsic properties calculations and on comparison with an ideal homogeneous image (joint entropy, mutual information, normalized cross correlation and index Q) as categorization parameters. The algorithms evaluation was performed on 100 cases of the mammographic data sets provided by the Ministerio de Salud de la Provincia de Córdoba, Argentina—Programa de Prevención del Cáncer de Mama (Department of Public Health, Córdoba, Argentina, Breast Cancer Prevention Program). The obtained breast classifications were compared with the expert medical diagnostics, showing a good performance. The implemented algorithms revealed a high potentiality to classify breasts into tissue density categories.-
dc.descriptionFil: Quintana Zurro, Clara Ines. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina-
dc.descriptionFil: Redondo, Marcelo. Universidad Nacional de Córdoba; Argentina-
dc.descriptionFil: Tirao, German Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Física Enrique Gaviola. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.formatapplication/pdf-
dc.languageeng-
dc.publisherPergamon-Elsevier Science Ltd.-
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.radphyschem.2013.10.006-
dc.relationinfo:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0969806X13005458-
dc.rightsinfo:eu-repo/semantics/restrictedAccess-
dc.rightshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/-
dc.sourcereponame:CONICET Digital (CONICET)-
dc.sourceinstname:Consejo Nacional de Investigaciones Científicas y Técnicas-
dc.sourceinstacron:CONICET-
dc.subjectBreast density classification-
dc.subjectMathematical processing-
dc.subjectComputer-aided diagnostic systems-
dc.subjectMammography-
dc.titleImplementation of several mathematical algorithms to breast tissue density classification-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.typeinfo:ar-repo/semantics/articulo-
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