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
- Institución
- Universidad Nacional de Córdoba
- OAI Identificador
- oai:rdu.unc.edu.ar:11086/25405
Ver los metadatos del registro completo
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/openAccess2025-09-29T13:42:37Zoai: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:25722025-09-29 13:42:38.044Repositorio 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_ |
1844618932496891904 |
score |
13.070432 |