Automated quality assessment in three-dimensional breast ultrasound images

Automated three-dimensional breast ultrasound (ABUS) is a valuable adjunct to x-ray mammography for breast cancer screening of women with dense breasts. High image quality is essential for proper diagnostics and computer-aided detection. We propose an automated image quality assessment system for AB...

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Detalles Bibliográficos
Autores: Schwaab, Julia, Diez, Yago, Oliver i Malagelada, Arnau, Martí Marly, Robert, Zelst, Jan Van, Gubern Mérida, Albert, Mourri, Ahmed Bensouda, Gregori, Johannes, Günther, Matthias
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2016
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/18461
Acceso en línea:http://hdl.handle.net/10256/18461
Access Level:acceso abierto
Palabra clave:Mama -- Ecografia
Breast -- Ultrasonic imaging
Imatges -- Processament
Imatgeria per al diagnòstic
Diagnostic imaging
Mama -- Càncer
Breast -- Cancer
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spelling Automated quality assessment in three-dimensional breast ultrasound imagesSchwaab, JuliaDiez, YagoOliver i Malagelada, ArnauMartí Marly, RobertZelst, Jan VanGubern Mérida, AlbertMourri, Ahmed BensoudaGregori, JohannesGünther, MatthiasMama -- EcografiaBreast -- Ultrasonic imagingImatges -- ProcessamentImatgeria per al diagnòsticDiagnostic imagingMama -- CàncerBreast -- CancerAutomated three-dimensional breast ultrasound (ABUS) is a valuable adjunct to x-ray mammography for breast cancer screening of women with dense breasts. High image quality is essential for proper diagnostics and computer-aided detection. We propose an automated image quality assessment system for ABUS images that detects artifacts at the time of acquisition. Therefore, we study three aspects that can corrupt ABUS images: the nipple position relative to the rest of the breast, the shadow caused by the nipple, and the shape of the breast contour on the image. Image processing and machine learning algorithms are combined to detect these artifacts based on 368 clinical ABUS images that have been rated manually by two experienced clinicians. At a specificity of 0.99, 55% of the images that were rated as low quality are detected by the proposed algorithms. The areas under the ROC curves of the single classifiers are 0.99 for the nipple position, 0.84 for the nipple shadow, and 0.89 for the breast contour shape. The proposed algorithms work fast and reliably, which makes them adequate for online evaluation of image quality during acquisition. The presented concept may be extended to further image modalities and quality aspectsSociety of Photo-optical Instrumentation Engineers (SPIE)2016info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/18461http://hdl.handle.net/10256/18461© Journal of Medical Imaging, 2016, vol. 3, p. 027002Articles publicats (D-ATC)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.1117/1.JMI.3.2.027002info:eu-repo/semantics/altIdentifier/issn/2329-4302info:eu-repo/semantics/altIdentifier/eissn/2329-4310Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/184612026-05-29T05:05:01Z
dc.title.none.fl_str_mv Automated quality assessment in three-dimensional breast ultrasound images
title Automated quality assessment in three-dimensional breast ultrasound images
spellingShingle Automated quality assessment in three-dimensional breast ultrasound images
Schwaab, Julia
Mama -- Ecografia
Breast -- Ultrasonic imaging
Imatges -- Processament
Imatgeria per al diagnòstic
Diagnostic imaging
Mama -- Càncer
Breast -- Cancer
title_short Automated quality assessment in three-dimensional breast ultrasound images
title_full Automated quality assessment in three-dimensional breast ultrasound images
title_fullStr Automated quality assessment in three-dimensional breast ultrasound images
title_full_unstemmed Automated quality assessment in three-dimensional breast ultrasound images
title_sort Automated quality assessment in three-dimensional breast ultrasound images
dc.creator.none.fl_str_mv Schwaab, Julia
Diez, Yago
Oliver i Malagelada, Arnau
Martí Marly, Robert
Zelst, Jan Van
Gubern Mérida, Albert
Mourri, Ahmed Bensouda
Gregori, Johannes
Günther, Matthias
author Schwaab, Julia
author_facet Schwaab, Julia
Diez, Yago
Oliver i Malagelada, Arnau
Martí Marly, Robert
Zelst, Jan Van
Gubern Mérida, Albert
Mourri, Ahmed Bensouda
Gregori, Johannes
Günther, Matthias
author_role author
author2 Diez, Yago
Oliver i Malagelada, Arnau
Martí Marly, Robert
Zelst, Jan Van
Gubern Mérida, Albert
Mourri, Ahmed Bensouda
Gregori, Johannes
Günther, Matthias
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Mama -- Ecografia
Breast -- Ultrasonic imaging
Imatges -- Processament
Imatgeria per al diagnòstic
Diagnostic imaging
Mama -- Càncer
Breast -- Cancer
topic Mama -- Ecografia
Breast -- Ultrasonic imaging
Imatges -- Processament
Imatgeria per al diagnòstic
Diagnostic imaging
Mama -- Càncer
Breast -- Cancer
description Automated three-dimensional breast ultrasound (ABUS) is a valuable adjunct to x-ray mammography for breast cancer screening of women with dense breasts. High image quality is essential for proper diagnostics and computer-aided detection. We propose an automated image quality assessment system for ABUS images that detects artifacts at the time of acquisition. Therefore, we study three aspects that can corrupt ABUS images: the nipple position relative to the rest of the breast, the shadow caused by the nipple, and the shape of the breast contour on the image. Image processing and machine learning algorithms are combined to detect these artifacts based on 368 clinical ABUS images that have been rated manually by two experienced clinicians. At a specificity of 0.99, 55% of the images that were rated as low quality are detected by the proposed algorithms. The areas under the ROC curves of the single classifiers are 0.99 for the nipple position, 0.84 for the nipple shadow, and 0.89 for the breast contour shape. The proposed algorithms work fast and reliably, which makes them adequate for online evaluation of image quality during acquisition. The presented concept may be extended to further image modalities and quality aspects
publishDate 2016
dc.date.none.fl_str_mv 2016
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/18461
http://hdl.handle.net/10256/18461
url http://hdl.handle.net/10256/18461
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1117/1.JMI.3.2.027002
info:eu-repo/semantics/altIdentifier/issn/2329-4302
info:eu-repo/semantics/altIdentifier/eissn/2329-4310
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Society of Photo-optical Instrumentation Engineers (SPIE)
publisher.none.fl_str_mv Society of Photo-optical Instrumentation Engineers (SPIE)
dc.source.none.fl_str_mv © Journal of Medical Imaging, 2016, vol. 3, p. 027002
Articles publicats (D-ATC)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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