dc.contributor.author | Quintana Zurro, Clara Inés | es |
dc.contributor.author | Redondo, Marcelo | es |
dc.contributor.author | Tirao, Germán Alfredo | es |
dc.date.accessioned | 2022-05-30T00:58:16Z | |
dc.date.issued | 2014 | |
dc.identifier.uri | http://hdl.handle.net/11086/25405 | |
dc.description.abstract | The 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. | es |
dc.format.medium | Impreso | es |
dc.language.iso | eng | en |
dc.relation | https://www.sciencedirect.com/science/article/abs/pii/S0969806X13005458 | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
dc.rights | restrictedAccess | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.source | ISSN: 0969-806X | en |
dc.subject | Breast density classification | en |
dc.subject | Mathematical processing | en |
dc.subject | Computer-aidedd diagnostic systems | en |
dc.subject | Mammography | en |
dc.title | Implementation of several mathematical algorithms to breast tissue density classification | en |
dc.type | article | es |
dc.description.version | publishedVersion | en |
dc.description.fil | Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. | es |
dc.description.fil | Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. | es |
dc.description.fil | Fil: Quintana Zurro, Clara Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina. | es |
dc.description.fil | Fil: Redondo, Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Médicas; Argentina | es |
dc.description.fil | Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. | es |
dc.description.fil | Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina. | es |
dc.description.fil | Fil: Tirao, Germán Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina. | es |
dc.journal.country | Reino Unido | es |
dc.journal.editorial | Elsevier | es |
dc.journal.pagination | 261-263 | es |
dc.journal.referato | Con referato | es |
dc.journal.title | Radiation Physics and Chemistry | en |
dc.journal.volume | 95 | en |
dc.description.field | Otras ciencias físicas | es |
dc.identifier.url | https://doi.org/10.1016/j.radphyschem.2013.10.006 | |
dc.identifier.doi | https://doi.org/10.1016/j.radphyschem.2013.10.006 | |