Implementation of several mathematical algorithms to breast tissue density classification

Autores
Quintana Zurro, Clara Ines; Redondo, Marcelo; Tirao, German Alfredo
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Fil: 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
Fil: Redondo, Marcelo. Universidad Nacional de Córdoba; Argentina
Fil: 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
Materia
Breast Density Classification
Mathematical Processing
Computer-Aided Diagnostic Systems
Mammography
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/31701
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Implementation of several mathematical algorithms to breast tissue density classificationQuintana Zurro, Clara InesRedondo, MarceloTirao, German AlfredoBreast Density ClassificationMathematical ProcessingComputer-Aided Diagnostic SystemsMammographyThe 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.Fil: 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; ArgentinaFil: Redondo, Marcelo. Universidad Nacional de Córdoba; ArgentinaFil: 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; ArgentinaPergamon-Elsevier Science Ltd.2014-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/31701Tirao, 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; 261-2630969-806XCONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.radphyschem.2013.10.006info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0969806X13005458info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:37:36Zoai:ri.conicet.gov.ar:11336/31701instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:37:37.153CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
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 Ines
Breast Density Classification
Mathematical Processing
Computer-Aided 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 Ines
Redondo, Marcelo
Tirao, German Alfredo
author Quintana Zurro, Clara Ines
author_facet Quintana Zurro, Clara Ines
Redondo, Marcelo
Tirao, German Alfredo
author_role author
author2 Redondo, Marcelo
Tirao, German Alfredo
author2_role author
author
dc.subject.none.fl_str_mv Breast Density Classification
Mathematical Processing
Computer-Aided Diagnostic Systems
Mammography
topic Breast Density Classification
Mathematical Processing
Computer-Aided Diagnostic Systems
Mammography
dc.description.none.fl_txt_mv 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.
Fil: 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
Fil: Redondo, Marcelo. Universidad Nacional de Córdoba; Argentina
Fil: 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
description 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.
publishDate 2014
dc.date.none.fl_str_mv 2014-02
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/11336/31701
Tirao, 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
0969-806X
CONICET Digital
CONICET
url http://hdl.handle.net/11336/31701
identifier_str_mv Tirao, 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
0969-806X
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.radphyschem.2013.10.006
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0969806X13005458
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd.
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd.
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.070432

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