Table of Contents Author Guidelines Submit a Manuscript
International Journal of Breast Cancer
Volume 2015, Article ID 276217, 31 pages
http://dx.doi.org/10.1155/2015/276217
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 [28 citations]

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

  • Jonathan Hernandez-Capistran, and Jorge F. Martinez-Carballido, “Thresholding methods review for microcalcifications segmentation on mammography images in obvious, subtle, and cluster categories,” 2016 13th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), pp. 1–6, . View at Publisher · View at Google Scholar
  • Andrik Rampun, Hui Wang, Bryan Scotney, Philip Morrow, and Reyer Zwiggelaar, “Classification of mammographic microcalcification clusters with machine learning confidence levels,” 14th International Workshop on Breast Imaging (IWBI 2018), pp. 35, . View at Publisher · View at Google Scholar
  • 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
  • Wenda He, Sam Harvey, Arne Juette, Erika R. E. Denton, and Reyer Zwiggelaar, “Mammographic Segmentation and Density Classification: A Fractal Inspired Approach,” Breast Imaging, vol. 9699, pp. 359–366, 2016. View at Publisher · View at Google Scholar
  • Minu George, Andrik Rampun, Erika Denton, and Reyer Zwiggelaar, “Mammographic Ellipse Modelling Towards Birads Density Classification,” Breast Imaging, vol. 9699, pp. 423–430, 2016. View at Publisher · View at Google Scholar
  • Giulio Tagliafico, and Alberto Tagliaficopp. 29–44, 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
  • Mickaël Garnier, Keith Humphreys, and Maya Alsheh Ali, “Spatial Relations of Mammographic Density Regions and their Association with Breast Cancer Risk,” Procedia Computer Science, vol. 90, pp. 169–174, 2016. View at Publisher · View at Google Scholar
  • Erika Denton, Minu George, and Reyer Zwiggelaar, “Mammographic Ellipse Modelling for Risk Estimation,” Procedia Computer Science, vol. 90, pp. 163–168, 2016. View at Publisher · View at Google Scholar
  • Anna K. Jerebko, Thomas Mertelmeier, and Andreas Fieselmann, “Volumetric breast density combined with masking risk: Enhanced characterization of breast density from mammography images,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9699, pp. 486–492, 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
  • Daniel Förnvik, Masako Kataoka, Akane Ohashi, Mami Iima, Shotaro Kanao, Masakazu Toi, and Kaori Togashi, “The role of breast tomosynthesis in a predominantly dense breast population at a tertiary breast centre: breast density assessment and diagnostic performance in comparison with MRI,” European Radiology, vol. 28, no. 8, pp. 3194–3203, 2018. 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
  • Erin E E Fowler, Autumn Smallwood, Cassandra Miltich, Jennifer Drukteinis, Thomas A Sellers, and John Heine, “Generalized breast density metrics,” Physics in Medicine & Biology, vol. 64, no. 1, pp. 015006, 2018. View at Publisher · View at Google Scholar
  • Azam Hamidinekoo, Zobia Suhail, Erika Denton, and Reyer Zwiggelaar, “Comparing the performance of various deep networks for binary classification of breast tumours,” Progress in Biomedical Optics and Imaging - Proceedings of SPIE, vol. 10718, 2018. View at Publisher · View at Google Scholar
  • M. Inmaculada García, Karen López-Linares, Andrik Rampun, Bryan W. Scotney, Philip J. Morrow, Remi Salmon, Marc Garbey, Stefan Audersch, Marina Azpíroz, Juan A. Romero, Vicente Belloch, José M. Santabárbara, and Ivan Macia, “Advanced Image Processing Algorithms for Breast Cancer Decision Support and Information Management System,” Innovation in Medicine and Healthcare Systems, and Multimedia, vol. 145, pp. 147–156, 2019. View at Publisher · View at Google Scholar
  • Suhas Sapate, Sanjay Talbar, Abhishek Mahajan, Nilesh Sable, Subhash Desai, and Meenakshi Thakur, “Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms,” Biocybernetics and Biomedical Engineering, 2019. View at Publisher · View at Google Scholar
  • Erin E E Fowler, Cassandra Hathaway, Fabryann Tillman, Robert Weinfurtner, Thomas A Sellers, and John Heine, “Spatial correlation and breast cancer risk,” Biomedical Physics & Engineering Express, vol. 5, no. 4, pp. 045007, 2019. View at Publisher · View at Google Scholar
  • Alan W. Grogono, “Acid–Base Reports Need a Text Explanation,” Anesthesiology, vol. 130, no. 4, pp. 668–669, 2019. View at Publisher · View at Google Scholar
  • Andreas Fieselmann, and Daniel Förnvik, “Volumetric breast density measurement for personalized screening: accuracy, reproducibility, consistency, and agreement with visual assessment,” Journal of Medical Imaging, vol. 6, no. 03, pp. 1, 2019. View at Publisher · View at Google Scholar
  • Minu George, and Reyer Zwiggelaar, “Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring,” Journal of Imaging, vol. 5, no. 2, pp. 24, 2019. View at Publisher · View at Google Scholar
  • Ali Mohammad Alqudah, Huda M. S. Algharib, Amal M. S. Algharib, and Hanan M. S. Algharib, “Computer Aided Diagnosis System For Automatic Two Stages Classification Of Breast Mass In Digital Mammogram Images,” Biomedical Engineering: Applications, Basis and Communications, vol. 31, no. 01, pp. 1950007, 2019. View at Publisher · View at Google Scholar
  • Azam Hamidinekoo, Zaineb Chelly Dagdia, Zobia Suhail, and Reyer Zwiggelaar, “Distributed Rough Set Based Feature Selection Approach to Analyse Deep and Hand-crafted Features for Mammography Mass Classification,” Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, pp. 2423–2432, 2019. View at Publisher · View at Google Scholar