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...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Breast Cancer |
| Online Access: | http://dx.doi.org/10.1155/2015/276217 |
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| _version_ | 1832565538715336704 |
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| author | Wenda He Arne Juette Erika R. E. Denton Arnau Oliver Robert Martí Reyer Zwiggelaar |
| author_facet | Wenda He Arne Juette Erika R. E. Denton Arnau Oliver Robert Martí Reyer Zwiggelaar |
| author_sort | Wenda He |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-dab7112d69b6481fadba4d174dec2719 |
| institution | Kabale University |
| issn | 2090-3170 2090-3189 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Breast Cancer |
| spelling | doaj-art-dab7112d69b6481fadba4d174dec27192025-02-03T01:07:21ZengWileyInternational Journal of Breast Cancer2090-31702090-31892015-01-01201510.1155/2015/276217276217A Review on Automatic Mammographic Density and Parenchymal SegmentationWenda He0Arne Juette1Erika R. E. Denton2Arnau Oliver3Robert Martí4Reyer Zwiggelaar5Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UKDepartment of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UKDepartment of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UKDepartment of Architecture and Computer Technology, University of Girona, 17071 Girona, SpainDepartment of Architecture and Computer Technology, University of Girona, 17071 Girona, SpainDepartment of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UKBreast 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.http://dx.doi.org/10.1155/2015/276217 |
| spellingShingle | Wenda He Arne Juette Erika R. E. Denton Arnau Oliver Robert Martí Reyer Zwiggelaar A Review on Automatic Mammographic Density and Parenchymal Segmentation International Journal of Breast Cancer |
| title | 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_short | A Review on Automatic Mammographic Density and Parenchymal Segmentation |
| title_sort | review on automatic mammographic density and parenchymal segmentation |
| url | http://dx.doi.org/10.1155/2015/276217 |
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