Implementation of several mathematical algorithms to breast tissue density classification

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 t...

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Detalles Bibliográficos
Autores principales: Quintana Zurro, Clara Inés, Redondo, Marcelo, Tirao, Germán Alfredo
Formato: article
Lenguaje:Inglés
Publicado: 2022
Materias:
Breast density classification
Mathematical processing
Computer-aidedd diagnostic systems
Mammography
Acceso en línea: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
Aporte de:
Repositorio Digital Universitario (UNC) de Universidad Nacional de Córdoba
id I10-R141-11086-25405
record_format dspace
institution Universidad Nacional de Córdoba
institution_str I-10
repository_str R-141
collection Repositorio Digital Universitario (UNC)
language Inglés
topic Breast density classification
Mathematical processing
Computer-aidedd diagnostic systems
Mammography
spellingShingle Breast density classification
Mathematical processing
Computer-aidedd diagnostic systems
Mammography
Quintana Zurro, Clara Inés
Redondo, Marcelo
Tirao, Germán Alfredo
Implementation of several mathematical algorithms to breast tissue density classification
topic_facet Breast density classification
Mathematical processing
Computer-aidedd diagnostic systems
Mammography
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.
format article
author Quintana Zurro, Clara Inés
Redondo, Marcelo
Tirao, Germán Alfredo
author_facet Quintana Zurro, Clara Inés
Redondo, Marcelo
Tirao, Germán Alfredo
author_sort Quintana Zurro, Clara Inés
title Implementation of several mathematical algorithms to breast tissue density classification
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
publishDate 2022
url 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
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AT redondomarcelo implementationofseveralmathematicalalgorithmstobreasttissuedensityclassification
AT tiraogermanalfredo implementationofseveralmathematicalalgorithmstobreasttissuedensityclassification
bdutipo_str Repositorios
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