A review on automatic mammographic density and parenchymal segmentation
Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast...
| Autores: | , , , , , |
|---|---|
| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| 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:10256/16939 |
| Acceso en línea: | http://hdl.handle.net/10256/16939 |
| Access Level: | acceso abierto |
| Palabra clave: | Imatges -- Segmentació Imaging segmentation Imatgeria mèdica Imaging systems in medicine Mama -- Radiografia Breast -- Radiography Diagnòstic per la imatge Diagnostic imaging Radiografia mèdica -- Tècniques digitals Radiography, Medical -- Digital techniques |
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A review on automatic mammographic density and parenchymal segmentationHe, WendaJuette, ArneDenton, Erika R. E.Oliver i Malagelada, ArnauMartí Marly, RobertZwiggelaar, ReyerImatges -- SegmentacióImaging segmentationImatgeria mèdicaImaging systems in medicineMama -- RadiografiaBreast -- RadiographyDiagnòstic per la imatgeDiagnostic imagingRadiografia mèdica -- Tècniques digitalsRadiography, Medical -- Digital techniquesBreast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction modelsHindawi2015info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/16939http://hdl.handle.net/10256/16939International Journal of Breast Cancer, 2015, vol. 2015, art ID 276217Articles 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/10.1155/2015/276217info:eu-repo/semantics/altIdentifier/issn/2090-3170Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/169392026-05-29T05:05:01Z |
| dc.title.none.fl_str_mv |
A review on automatic mammographic density and parenchymal segmentation |
| title |
A review on automatic mammographic density and parenchymal segmentation |
| spellingShingle |
A review on automatic mammographic density and parenchymal segmentation He, Wenda Imatges -- Segmentació Imaging segmentation Imatgeria mèdica Imaging systems in medicine Mama -- Radiografia Breast -- Radiography Diagnòstic per la imatge Diagnostic imaging Radiografia mèdica -- Tècniques digitals Radiography, Medical -- Digital techniques |
| title_short |
A review on automatic mammographic density and parenchymal segmentation |
| title_full |
A review on automatic mammographic density and parenchymal segmentation |
| title_fullStr |
A review on automatic mammographic density and parenchymal segmentation |
| title_full_unstemmed |
A review on automatic mammographic density and parenchymal segmentation |
| title_sort |
A review on automatic mammographic density and parenchymal segmentation |
| dc.creator.none.fl_str_mv |
He, Wenda Juette, Arne Denton, Erika R. E. Oliver i Malagelada, Arnau Martí Marly, Robert Zwiggelaar, Reyer |
| author |
He, Wenda |
| author_facet |
He, Wenda Juette, Arne Denton, Erika R. E. Oliver i Malagelada, Arnau Martí Marly, Robert Zwiggelaar, Reyer |
| author_role |
author |
| author2 |
Juette, Arne Denton, Erika R. E. Oliver i Malagelada, Arnau Martí Marly, Robert Zwiggelaar, Reyer |
| author2_role |
author author author author author |
| dc.subject.none.fl_str_mv |
Imatges -- Segmentació Imaging segmentation Imatgeria mèdica Imaging systems in medicine Mama -- Radiografia Breast -- Radiography Diagnòstic per la imatge Diagnostic imaging Radiografia mèdica -- Tècniques digitals Radiography, Medical -- Digital techniques |
| topic |
Imatges -- Segmentació Imaging segmentation Imatgeria mèdica Imaging systems in medicine Mama -- Radiografia Breast -- Radiography Diagnòstic per la imatge Diagnostic imaging Radiografia mèdica -- Tècniques digitals Radiography, Medical -- Digital techniques |
| description |
Breast cancer is the most frequently diagnosed cancer in women. However, the exact cause(s) of breast cancer still remains unknown. Early detection, precise identification of women at risk, and application of appropriate disease prevention measures are by far the most effective way to tackle breast cancer. There are more than 70 common genetic susceptibility factors included in the current non-image-based risk prediction models (e.g., the Gail and the Tyrer-Cuzick models). Image-based risk factors, such as mammographic densities and parenchymal patterns, have been established as biomarkers but have not been fully incorporated in the risk prediction models used for risk stratification in screening and/or measuring responsiveness to preventive approaches. Within computer aided mammography, automatic mammographic tissue segmentation methods have been developed for estimation of breast tissue composition to facilitate mammographic risk assessment. This paper presents a comprehensive review of automatic mammographic tissue segmentation methodologies developed over the past two decades and the evidence for risk assessment/density classification using segmentation. The aim of this review is to analyse how engineering advances have progressed and the impact automatic mammographic tissue segmentation has in a clinical environment, as well as to understand the current research gaps with respect to the incorporation of image-based risk factors in non-image-based risk prediction models |
| publishDate |
2015 |
| dc.date.none.fl_str_mv |
2015 |
| 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/10256/16939 http://hdl.handle.net/10256/16939 |
| url |
http://hdl.handle.net/10256/16939 |
| dc.language.none.fl_str_mv |
Inglés |
| language_invalid_str_mv |
Inglés |
| dc.relation.none.fl_str_mv |
info:eu-repo/semantics/altIdentifier/doi/10.1155/2015/276217 info:eu-repo/semantics/altIdentifier/issn/2090-3170 |
| 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 |
Hindawi |
| publisher.none.fl_str_mv |
Hindawi |
| dc.source.none.fl_str_mv |
International Journal of Breast Cancer, 2015, vol. 2015, art ID 276217 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) |
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Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
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Recercat. Dipósit de la Recerca de Catalunya |
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Recercat. Dipósit de la Recerca de Catalunya |
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15.812429 |