Implementation of several mathematical algorithms to breast tissue density classification

Autores
Quintana Zurro, Clara Inés; Redondo, Marcelo; Tirao, Germán Alfredo
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.
Fil: Quintana Zurro, Clara Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.
Fil: Redondo, Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Médicas; Argentina
Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.
Fil: Tirao, Germán Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.
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.
publishedVersion
Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.
Fil: Quintana Zurro, Clara Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.
Fil: Redondo, Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Médicas; Argentina
Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.
Fil: Tirao, Germán Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.
Otras ciencias físicas
Fuente
ISSN: 0969-806X
Materia
Breast density classification
Mathematical processing
Computer-aidedd diagnostic systems
Mammography
Nivel de accesibilidad
acceso abierto
Condiciones de uso
Repositorio
Repositorio Digital Universitario (UNC)
Institución
Universidad Nacional de Córdoba
OAI Identificador
oai:rdu.unc.edu.ar:11086/25405
Acceder
id RDUUNC_b5444c1ef9e41f77788ade858971ae03
oai_identifier_str oai:rdu.unc.edu.ar:11086/25405
network_acronym_str RDUUNC
repository_id_str 2572
network_name_str Repositorio Digital Universitario (UNC)
spelling Implementation of several mathematical algorithms to breast tissue density classificationQuintana Zurro, Clara InésRedondo, MarceloTirao, Germán AlfredoBreast density classificationMathematical processingComputer-aidedd diagnostic systemsMammographyFil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.Fil: Quintana Zurro, Clara Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.Fil: Redondo, Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Médicas; ArgentinaFil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.Fil: Tirao, Germán Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.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.publishedVersionFil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.Fil: Quintana Zurro, Clara Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.Fil: Redondo, Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Médicas; ArgentinaFil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.Fil: Tirao, Germán Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.Otras ciencias físicas2014info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/11086/25405https://doi.org/10.1016/j.radphyschem.2013.10.006https://doi.org/10.1016/j.radphyschem.2013.10.006ISSN: 0969-806Xreponame:Repositorio Digital Universitario (UNC)instname:Universidad Nacional de Córdobainstacron:UNCenghttps://www.sciencedirect.com/science/article/abs/pii/S0969806X13005458info:eu-repo/semantics/openAccess2025-10-23T11:17:00Zoai:rdu.unc.edu.ar:11086/25405Institucionalhttps://rdu.unc.edu.ar/Universidad públicaNo correspondehttp://rdu.unc.edu.ar/oai/snrdoca.unc@gmail.comArgentinaNo correspondeNo correspondeNo correspondeopendoar:25722025-10-23 11:17:00.75Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdobafalse
dc.title.none.fl_str_mv Implementation of several mathematical algorithms to breast tissue density classification
title Implementation of several mathematical algorithms to breast tissue density classification
spellingShingle Implementation of several mathematical algorithms to breast tissue density classification
Quintana Zurro, Clara Inés
Breast density classification
Mathematical processing
Computer-aidedd diagnostic systems
Mammography
title_short Implementation of several mathematical algorithms to breast tissue density classification
title_full Implementation of several mathematical algorithms to breast tissue density classification
title_fullStr Implementation of several mathematical algorithms to breast tissue density classification
title_full_unstemmed Implementation of several mathematical algorithms to breast tissue density classification
title_sort Implementation of several mathematical algorithms to breast tissue density classification
dc.creator.none.fl_str_mv Quintana Zurro, Clara Inés
Redondo, Marcelo
Tirao, Germán Alfredo
author Quintana Zurro, Clara Inés
author_facet Quintana Zurro, Clara Inés
Redondo, Marcelo
Tirao, Germán Alfredo
author_role author
author2 Redondo, Marcelo
Tirao, Germán Alfredo
author2_role author
author
dc.subject.none.fl_str_mv Breast density classification
Mathematical processing
Computer-aidedd diagnostic systems
Mammography
topic Breast density classification
Mathematical processing
Computer-aidedd diagnostic systems
Mammography
dc.description.none.fl_txt_mv Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.
Fil: Quintana Zurro, Clara Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.
Fil: Redondo, Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Médicas; Argentina
Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.
Fil: Tirao, Germán Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.
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.
publishedVersion
Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.
Fil: Quintana Zurro, Clara Inés. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.
Fil: Redondo, Marcelo. Universidad Nacional de Córdoba. Facultad de Ciencias Médicas; Argentina
Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
Fil: Tirao, Germán Alfredo. Universidad Nacional de Córdoba. Instituto de Física Enrique Gaviola; Argentina.
Fil: Tirao, Germán Alfredo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Física Enrique Gaviola; Argentina.
Otras ciencias físicas
description Fil: Quintana Zurro, Clara Inés. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina.
publishDate 2014
dc.date.none.fl_str_mv 2014
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11086/25405
https://doi.org/10.1016/j.radphyschem.2013.10.006
https://doi.org/10.1016/j.radphyschem.2013.10.006
url http://hdl.handle.net/11086/25405
https://doi.org/10.1016/j.radphyschem.2013.10.006
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.sciencedirect.com/science/article/abs/pii/S0969806X13005458
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv ISSN: 0969-806X
reponame:Repositorio Digital Universitario (UNC)
instname:Universidad Nacional de Córdoba
instacron:UNC
reponame_str Repositorio Digital Universitario (UNC)
collection Repositorio Digital Universitario (UNC)
instname_str Universidad Nacional de Córdoba
instacron_str UNC
institution UNC
repository.name.fl_str_mv Repositorio Digital Universitario (UNC) - Universidad Nacional de Córdoba
repository.mail.fl_str_mv oca.unc@gmail.com
_version_ 1846785270519693312
score 12.982451

Publicaciones similares