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International Journal of Breast Cancer
Volume 2015, Article ID 276217, 31 pages
Review Article

A Review on Automatic Mammographic Density and Parenchymal Segmentation

1Department of Computer Science, Aberystwyth University, Aberystwyth SY23 3DB, UK
2Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, UK
3Department of Architecture and Computer Technology, University of Girona, 17071 Girona, Spain

Received 13 January 2015; Revised 21 April 2015; Accepted 17 May 2015

Academic Editor: Mireille Broeders

Copyright © 2015 Wenda He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [9 citations]

The following is the list of published articles that have cited the current article.

  • Rikke Rass Winkel, Kersten Petersen, Martin Lillholm, Michael Bachmann Nielsen, Elsebeth Lynge, Wei Yao Uldall, My von Euler-Chelpin, Mads Nielsen, and Ilse Vejborg, “Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: A case-control study,” BMC Cancer, vol. 16, no. 1, 2016. View at Publisher · View at Google Scholar
  • Michiel Kallenberg, Kersten Petersen, Mads Nielsen, Andrew Y. Ng, Pengfei Diao, Christian Igel, Celine M. Vachon, Katharina Holland, Rikke Rass Winkel, Nico Karssemeijer, and Martin Lillholm, “Unsupervised Deep Learning Applied to Breast Density Segmentation and Mammographic Risk Scoring,” IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1322–1331, 2016. View at Publisher · View at Google Scholar
  • Brian L. Sprague, Donald L. Weaver, Emily F. Conant, Tracy Onega, Michael P. Garcia, Elisabeth F. Beaber, Constance D. Lehman, Ronilda Lacson, Mitchell D. Schnall, Despina Kontos, Jennifer S. Haas, William E. Barlow, Sally D. Herschorn, and Anna N.A. Tosteson, “Variation in Mammographic Breast Density Assessments among Radiologists in Clinical Practice: A Multicenter Observational Study,” Annals of Internal Medicine, vol. 165, no. 7, pp. 457–464, 2016. View at Publisher · View at Google Scholar
  • Maya Alsheh Ali, Kamila Czene, Louise Eriksson, Per Hall, and Keith Humphreys, “Breast Tissue Organisation and its Association with Breast Cancer Risk,” Breast Cancer Research, vol. 19, no. 1, 2017. View at Publisher · View at Google Scholar
  • Stamatia Destounis, Andrea Arieno, Renee Morgan, Christina Roberts, and Ariane Chan, “Qualitative Versus Quantitative Mammographic Breast Density Assessment: Applications for the US and Abroad,” Diagnostics, vol. 7, no. 2, pp. 30, 2017. View at Publisher · View at Google Scholar
  • Katharina Holland, Albert Gubern-Mérida, Ritse M Mann, and Nico Karssemeijer, “Optimization of volumetric breast density estimation in digital mammograms,” Physics in Medicine and Biology, vol. 62, no. 9, pp. 3779–3797, 2017. View at Publisher · View at Google Scholar
  • Katharina Holland, Carla H. van Gils, Ritse M. Mann, and Nico Karssemeijer, “Quantification of masking risk in screening mammography with volumetric breast density maps,” Breast Cancer Research and Treatment, 2017. View at Publisher · View at Google Scholar
  • Syed Jamal Safdar Gardezi, Faouzi Adjed, Ibrahima Faye, Nidal Kamel, and Mohamed Meselhy Eltoukhy, “Segmentation of pectoral muscle using the adaptive gamma corrections,” Multimedia Tools and Applications, 2017. View at Publisher · View at Google Scholar
  • Azam Hamidinekoo, Erika Denton, Andrik Rampun, Kate Honnor, and Reyer Zwiggelaar, “Deep Learning in Mammography and Breast Histology, an Overview and Future Trends,” Medical Image Analysis, 2018. View at Publisher · View at Google Scholar