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International Journal of Breast Cancer
Volume 2015 (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.

Linked References

  1. J. Ferlay, H.-R. Shin, F. Bray, D. Forman, C. Mathers, and D. M. Parkin, “Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008,” International Journal of Cancer, vol. 127, no. 12, pp. 2893–2917, 2010. View at Publisher · View at Google Scholar · View at Scopus
  2. S. A. Eccles, E. Aboagye, S. Ali et al., “Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer,” Breast Cancer Research, vol. 15, article R92, 2013. View at Google Scholar
  3. H. Darabi, K. Czene, W. Zhao, J. Liu, P. Hall, and K. Humphreys, “Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement,” Breast Cancer Research, vol. 14, no. 1, article R25, 2012. View at Publisher · View at Google Scholar · View at Scopus
  4. N. F. Boyd, L. J. Martin, M. J. Yaffe, and S. Minkin, “Mammographic density and breast cancer risk: current understanding and future prospects,” Breast Cancer Research, vol. 13, no. 6, article 223, 2011. View at Publisher · View at Google Scholar · View at Scopus
  5. American College of Radiology, Breast Imaging Reporting and Data System (BI-RADS), American College of Radiology, Reston, Va, USA, 2003.
  6. A. Karellas, “Mammographic pattern analysis: an emerging risk assessment tool,” Academic Radiology, vol. 14, no. 5, pp. 511–512, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. J. A. Harvey, G. J. Stukenborg, W. F. Cohn et al., “Volumetric breast density improves breast cancer risk prediction,” Oncology Times, vol. 37, no. 6, p. 33, 2015, Proceedings of the San Antonio Breast Cancer Symposium 2014. View at Publisher · View at Google Scholar
  8. M. H. Gail, L. A. Brinton, D. P. Byar et al., “Projecting individualized probabilities of developing breast cancer for white females who are being examined annually,” Journal of the National Cancer Institute, vol. 81, no. 24, pp. 1879–1886, 1989. View at Publisher · View at Google Scholar · View at Scopus
  9. J. Tyrer, S. W. Duffy, and J. Cuzick, “A breast cancer prediction model incorporating familial and personal risk factors,” Statistics in Medicine, vol. 23, no. 7, pp. 1111–1130, 2004. View at Google Scholar
  10. A. Eng, Z. Gallant, J. Shepherd et al., “Volumetric breast density improves breast cancer risk prediction,” Breast Cancer Research, vol. 16, no. 5, article 439, 2014. View at Google Scholar
  11. D. G. R. Evans, J. Warwick, S. M. Astley et al., “Assessing individual breast cancer risk within the U.K. National Health Service Breast Screening Program: a new paradigm for cancer prevention,” Cancer Prevention Research, vol. 5, no. 7, pp. 943–951, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. C. H. van Gils, J. D. M. Otten, A. L. M. Verbeek, and J. H. C. L. Hendriks, “Mammographic breast density and risk of breast cancer: masking bias or causality?” European Journal of Epidemiology, vol. 14, no. 4, pp. 315–320, 1998. View at Publisher · View at Google Scholar · View at Scopus
  13. J. N. Wolfe, “Breast patterns as an index of risk for developing breast cancer,” American Journal of Roentgenology, vol. 126, no. 6, pp. 1130–1139, 1976. View at Publisher · View at Google Scholar · View at Scopus
  14. E. A. Sickles, “Wolfe mammographic parenchymal patterns and breast cancer risk,” American Journal of Roentgenology, vol. 188, no. 2, pp. 301–303, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Tabár and P. B. Dean, “Mammographic parenchymal patterns: risk indicator for breast cancer?” The Journal of the American Medical Association, vol. 247, no. 2, pp. 185–189, 1982. View at Publisher · View at Google Scholar · View at Scopus
  16. L. Tabár, T. Tot, and P. B. Dean, The Art and Science of Early Detection with Mamography: Perception, Interpretation, Histopatholigic Correlation, Georg Thieme, 1st edition, 2004.
  17. N. F. Boyd, J. W. Byng, R. A. Jong et al., “Quantitative classification of mammographic densities and breast cancer risk: results from the Canadian National Breast Screening Study,” Journal of the National Cancer Institute, vol. 87, no. 9, pp. 670–675, 1995. View at Publisher · View at Google Scholar · View at Scopus
  18. N. F. Boyd, L. J. Martin, L. Sun et al., “Body size, mammographic density, and breast cancer risk,” Cancer Epidemiology Biomarkers & Prevention, vol. 15, no. 11, pp. 2086–2092, 2006. View at Publisher · View at Google Scholar · View at Scopus
  19. E. S. Burnside, E. A. Sickles, L. W. Bassett et al., “The ACR BI-RADS c experience: learning from history,” Journal of the American College of Radiology, vol. 6, no. 12, pp. 851–860, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. E. Sala, R. Warren, J. McCann, S. Duffy, N. Day, and R. Luben, “Mammographic parenchymal patterns and mode of detection: implications for the breast screening programme,” Journal of Medical Screening, vol. 5, no. 4, pp. 207–212, 1998. View at Publisher · View at Google Scholar · View at Scopus
  21. J. C. Bailar III, “Mammography: a contrary view,” Annals of Internal Medicine, vol. 84, no. 1, pp. 77–84, 1976. View at Publisher · View at Google Scholar · View at Scopus
  22. I. T. Gram, E. Funkhouser, and L. Tabár, “The Tabár classification of mammographic parenchymal patterns,” European Journal of Radiology, vol. 24, no. 2, pp. 131–136, 1997. View at Publisher · View at Google Scholar · View at Scopus
  23. I. T. Gram, Y. Bremnes, G. Ursin, G. Maskarinec, N. Bjurstam, and E. Lund, “Percentage density, Wolfe's and Tabár's mammographic patterns: agreement and association with risk factors for breast cancer,” Breast Cancer Research, vol. 7, no. 5, pp. R854–R861, 2005. View at Publisher · View at Google Scholar · View at Scopus
  24. I. Muhimmah, A. Oliver, E. R. E. Denton, J. Pont, E. Pérez, and R. Zwiggelaar, “Comparison between wolfe, boyd, BI-RADS and Tabár based mammographic risk assessment,” in Digital Mammography, vol. 4046 of Lecture Notes in Computer Science, pp. 407–415, Springer, 2006. View at Google Scholar
  25. T. Matsubara, D. Yamazaki, H. Fujita, T. Hara, T. Iwase, and T. Endo, “An automated classification method for mammograms based on evaluation of fibroglandular breast tissue density,” in Proceedings of the International Workshop on Digital Mammography, pp. 737–741, 2000.
  26. P. K. Saha, J. K. Udupa, E. F. Conant, D. P. Chakraborty, and D. Sullivan, “Breast tissue density quantification via digitized mammograms,” IEEE Transactions on Medical Imaging, vol. 20, no. 8, pp. 792–803, 2001. View at Publisher · View at Google Scholar · View at Scopus
  27. R. Sivaramakrishna, N. A. Obuchowski, W. A. Chilcote, and K. A. Powell, “Automatic segmentation of mammographic density,” Academic Radiology, vol. 8, no. 3, pp. 250–256, 2001. View at Publisher · View at Google Scholar · View at Scopus
  28. C. Olsén and A. Mukhdoomi, “Automatic segmentation of fibroglandular tissue,” in Image Analysis, vol. 4522 of Lecture Notes in Computer Science, pp. 679–688, Springer, Berlin, Germany, 2007. View at Google Scholar
  29. S. D. Tzikopoulos, M. E. Mavroforakis, H. V. Georgiou, N. Dimitropoulos, and S. Theodoridis, “A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry,” Computer Methods and Programs in Biomedicine, vol. 102, no. 1, pp. 47–63, 2011. View at Publisher · View at Google Scholar · View at Scopus
  30. L.-J. W. Lu, T. K. Nishino, T. Khamapirad, J. J. Grady, M. H. Leonard Jr., and D. G. Brunder, “Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit,” Physics in Medicine and Biology, vol. 52, no. 16, pp. 4905–4921, 2007. View at Publisher · View at Google Scholar · View at Scopus
  31. R. J. Ferrari, R. M. Rangayyan, J. E. L. Desautels, and A. F. Frère, “Segmentation of mammograms: identification of the skin-air boundary, pectoral muscle, and fibroglandular disc,” in Proceedings of the 5th International Workshop on Digital Mammography (IWDM '00), pp. 573–579, Toronto, Canada, June 2000.
  32. R. J. Ferrari, R. M. Rangayyan, R. A. Borges, and A. F. Frère, “Segmentation of the fibro-glandular disc in mammograms using Gaussian mixture modelling,” Medical and Biological Engineering and Computing, vol. 42, no. 3, pp. 378–387, 2004. View at Publisher · View at Google Scholar · View at Scopus
  33. A. El-Zaart, “Expectation-maximization technique for fibro-glandular discs detection in mammography images,” Computers in Biology and Medicine, vol. 40, no. 4, pp. 392–401, 2010. View at Publisher · View at Google Scholar · View at Scopus
  34. C. Zhou, H. P. Chan, N. Petrick et al., “Computerized image analysis: estimation of breast density on mammograms,” Medical Physics, vol. 28, no. 6, pp. 1056–1069, 2001. View at Publisher · View at Google Scholar · View at Scopus
  35. J. T. Neyhart, M. Kirlakovsky, L. M. Coleman, R. Polikar, M. Tseng, and S. A. Mandayam, “Automated segmentation and quantitative characterization of radiodense tissue in digitized mammograms,” AIP Conference Proceedings, vol. 615, no. 1, pp. 1866–1873, 2002. View at Google Scholar
  36. Y. Kim, C. Kim, and J. H. Kim, “Automated estimation of breast density on mammogram using combined information of histogram statistics and boundary gradients,” in Medical Imaging 2010: Computer-Aided Diagnosis, vol. 7624 of Proceedings of SPIE, p. 76242F, San Diego, Calif, USA, March 2010. View at Publisher · View at Google Scholar
  37. C. Nickson, Y. Arzhaeva, Z. Aitken et al., “AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes,” Breast Cancer Research, vol. 15, no. 5, article R80, 2013. View at Publisher · View at Google Scholar · View at Scopus
  38. A. Oliver, J. Freixenet, A. Bosch, D. Raba, and R. Zwiggelaar, “Automatic classification of breast tissue,” in Proceedings of the 2nd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA '05), vol. 3523 of Lecture Notes in Computer Science, pp. 431–438, June 2005. View at Scopus
  39. H. Strange, E. R. E. Denton, M. Kibiro, and R. Zwiggelaar, “Manifold learning for density segmentation in high risk mammograms,” in Pattern Recognition and Image Analysis, vol. 7887 of Lecture Notes in Computer Science, pp. 245–252, Springer, Berlin, Germany, 2013. View at Publisher · View at Google Scholar
  40. K. Marias, M. G. Linguraru, M. A. G. Ballester, S. Petroudi, M. Tsiknakis, and S. M. Brady, “Automatic labelling and BI-RADS characterisation of mammogram densities,” in Proceedings of the 27th Annual International Conference of the Engineering in Medicine and Biology Society (EMBS '05), pp. 6394–6398, IEEE, September 2005. View at Scopus
  41. A. Oliver, J. Freixenet, and R. Zwiggelaar, “Automatic classification of breast density,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '05), vol. 2, pp. 1258–1261, September 2005. View at Publisher · View at Google Scholar · View at Scopus
  42. A. Oliver, J. Martí, J. Freixenet, J. Pont, and R. Zwiggelaar, “Automatic classification of breast density according BIRADS categories using a clustering approach,” in Proceedings of the Computed Aided Radiology and Surgery (CARS '05), Berlin, Germany, June 2005.
  43. A. Oliver, X. Lladó, R. Martı, J. Freixenet, and R. Zwiggelaar, “Classifying mammograms using texture information,” in Proceedings of the Medical Image Understanding and Analysis Conference, pp. 223–227, July 2007.
  44. A. Oliver, J. Freixenet, R. Martí et al., “A novel breast tissue density classification methodology,” IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 1, pp. 55–65, 2008. View at Publisher · View at Google Scholar · View at Scopus
  45. A. Oliver, J. Freixenet, R. Martí, and R. Zwiggelaar, “A comparison of breast tissue classification techniques,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2006, vol. 4191 of Lecture Notes in Computer Science, pp. 872–879, Springer, Berlin, Germany, 2006. View at Publisher · View at Google Scholar
  46. A. Torrent, A. Bardera, A. Oliver et al., “Breast density segmentation: a comparison of clustering and region based techniques,” in Digital Mammography, vol. 5116 of Lecture Notes in Computer Science, pp. 9–16, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  47. M. Tortajada, A. Oliver, R. Martí et al., “Adapting breast density classification from digitized to full-field digital mammograms,” in Breast Imaging, vol. 7361 of Lecture Notes in Computer Science, pp. 561–568, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  48. Z. Chen and R. Zwiggelaar, “A modified fuzzy c-means algorithm for breast tissue density segmentation in mammograms,” in Proceedings of the 10th International Conference on Information Technology and Applications in Biomedicine (ITAB '10), pp. 1–4, IEEE, Corfu, Greece, November 2010. View at Publisher · View at Google Scholar · View at Scopus
  49. B. Keller, D. Nathan, Y. Wang et al., “Adaptive multi-cluster fuzzy c-means segmentation of breast parenchymal tissue in digital mammography,” in Medical Image Computing and Computer-Assisted Intervention—MICCAI 2011, vol. 6893 of Lecture Notes in Computer Science, pp. 562–569, Springer, 2011. View at Google Scholar
  50. B. M. Keller, D. L. Nathan, Y. Wang et al., “Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation,” Medical Physics, vol. 39, no. 8, pp. 4903–4917, 2012. View at Publisher · View at Google Scholar · View at Scopus
  51. S. R. Aylward, B. M. Hemminger, and E. D. Pisano, “Mixture modeling for digital mammogram display and analysis,” in Proceedings of the International Workshop on Digital Mammography, pp. 305–312, 1998.
  52. R. Zwiggelaar, P. Planiol, J. Martí et al., “EM texture segmentation of mammographic images,” in Proceedings of the International Workshop on Digital Mammography, pp. 223–227, Bremen, Germany, June 2002.
  53. R. Zwiggelaar, L. Blot, D. Raba, and E. R. E. Denton, “Set-permutation-occurrence matrix based texture segmentation,” in Pattern Recognition and Image Analysis, vol. 2652 of Lecture Notes in Computer Science, pp. 1099–1107, Springer, Berlin, Germany, 2003. View at Publisher · View at Google Scholar
  54. R. Zwiggelaar, L. Blot, D. Raba, and D. R. E. Erika, “Texture segmentation in mammograms,” in Medical Image Understanding and Analysis, 2003. View at Google Scholar
  55. R. Zwiggelaar and E. R. E. Denton, “Optimal segmentation of mammographic images,” in Proceedings of the International Workshop on Digital Mammography, pp. 751–757, 2004.
  56. S. E. Selvan, C. C. Xavier, N. Karssemeijer, J. Sequeira, R. A. Cherian, and B. Y. Dhala, “Parameter estimation in stochastic mammogram model by heuristic optimization techniques,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 4, pp. 685–695, 2006. View at Publisher · View at Google Scholar · View at Scopus
  57. P. Miller and S. Astley, “Classification of breast tissue by texture analysis,” Image and Vision Computing, vol. 10, no. 5, pp. 277–282, 1992. View at Publisher · View at Google Scholar · View at Scopus
  58. J. Suckling, D. R. Dance, E. Moskovic, D. J. Lewis, and S. G. Blacker, “Segmentation of mammograms using multiple linked self-organizing neural networks,” Medical Physics, vol. 22, no. 2, pp. 145–152, 1995. View at Publisher · View at Google Scholar · View at Scopus
  59. J. J. Heine and R. P. Velthuizen, “A statistical methodology for mammographic density detection,” Medical Physics, vol. 27, no. 12, pp. 2644–2651, 2000. View at Publisher · View at Google Scholar · View at Scopus
  60. J. J. Heine, M. J. Carston, C. G. Scott et al., “An automated approach for estimation of breast density,” Cancer Epidemiology Biomarkers & Prevention, vol. 17, no. 11, pp. 3090–3097, 2008. View at Publisher · View at Google Scholar · View at Scopus
  61. S. Petroudi and M. Brady, “Breast density segmentation using texture,” in Digital Mammography, vol. 4046 of Lecture Notes in Computer Science, pp. 609–615, Springer, Berlin, Germany, 2006. View at Publisher · View at Google Scholar
  62. Y. C. Gong, M. Brady, and S. Petroudi, “Texture based mammogram classification and segmentation,” in Digital Mammography, vol. 4046 of Lecture Notes in Computer Science, pp. 616–625, Springer, 2006. View at Google Scholar
  63. A. Oliver, X. Lladó, E. Pérez et al., “A statistical approach for breast density segmentation,” Journal of Digital Imaging, vol. 23, no. 5, pp. 527–537, 2010. View at Publisher · View at Google Scholar · View at Scopus
  64. R. Zwiggelaar and E. R. E. Denton, “Texture based segmentation,” in Digital Mammography, vol. 4046 of Lecture Notes in Computer Science, pp. 433–440, Springer, Berlin, Germany, 2006. View at Publisher · View at Google Scholar
  65. M. Adel, M. Rasigni, S. Bourennane, and V. Juhan, “Statistical segmentation of regions of interest on a mammographic image,” EURASIP Journal on Advances in Signal Processing, vol. 2007, Article ID 49482, 8 pages, 2007. View at Publisher · View at Google Scholar
  66. R. Zwiggelaar, “Local greylevel appearance histogram based texture segmentation,” in Digital Mammography, vol. 6136 of Lecture Notes in Computer Science, pp. 175–182, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  67. M. G. J. Kallenberg, M. Lokate, C. H. van Gils, and N. Karssemeijer, “Automatic breast density segmentation: an integration of different approaches,” Physics in Medicine and Biology, vol. 56, no. 9, pp. 2715–2729, 2011. View at Publisher · View at Google Scholar · View at Scopus
  68. J. Li, L. Szekely, L. Eriksson et al., “High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer,” Breast Cancer Research, vol. 14, no. 4, article R114, 2012. View at Publisher · View at Google Scholar · View at Scopus
  69. Z. Chen, E. R. E. Denton, and R. Zwiggelaar, “Topographic representation based breast density segmentation for mammographic risk assessment,” in Proceedings of the 19th IEEE International Conference on Image Processing (ICIP '12), pp. 1993–1996, October 2012. View at Publisher · View at Google Scholar · View at Scopus
  70. Z. Chen, A. Oliver, E. R. E. Denton, and R. Zwiggelaar, “Automated mammographic risk classification based on breast density estimation,” in Pattern Recognition and Image Analysis, vol. 7887 of Lecture Notes in Computer Science, pp. 237–244, Springer, Berlin, Germany, 2013. View at Publisher · View at Google Scholar
  71. I. Muhimmah, W. He, E. R. E. Denton, and R. Zwiggelaar, “Segmentation based on textons and mammographic building blocks,” in Medical Image Understanding and Analysis, pp. 228–232, 2007. View at Google Scholar
  72. W. He, I. Muhimmah, E. R. E. Denton, and R. Zwiggelaar, “Mammographic risk assessment based on Tabár mammographic building blocks,” in Pattern Recognition and Image Analysis: New Information Technologies, pp. 219–222, 2008. View at Google Scholar
  73. W. He, E. R. E. Denton, and R. Zwiggelaar, “Mammographic segmentation based on mammographic parenchymal patterns and spatial moments,” in Proceedings of the 9th International Conference on Information Technology and Applications in Biomedicine, pp. 1–4, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  74. W. He, E. R. E. Denton, K. Stafford, and R. Zwiggelaar, “Mammographic image segmentation and risk classification based on mammographic parenchymal patterns and geometric moments,” Biomedical Signal Processing and Control, vol. 6, no. 3, pp. 321–329, 2011. View at Publisher · View at Google Scholar · View at Scopus
  75. W. He, E. R. E. Denton, and R. Zwiggelaar, “Mammographic segmentation and risk classification using a novel binary model based bayes classifier,” in Breast Imaging, vol. 7361 of Lecture Notes in Computer Science, pp. 40–47, Springer, Berlin, Germany, 2012. View at Publisher · View at Google Scholar
  76. W. He, E. R. E. Denton, and R. Zwiggelaar, “A novel breast image preprocessing for full field digital mammographic segmentation and risk classification,” in Medical Image Understanding and Analysis, pp. 40–47, 2014. View at Google Scholar
  77. W. He, E. R. E. Denton, and R. Zwiggelaar, “Mammographic image segmentation and risk classification using a novel texture signature based methodology,” in Digital Mammography, vol. 6136 of Lecture Notes in Computer Science, pp. 526–533, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  78. W. He and R. Zwiggelaar, “Breast parenchymal pattern analysis in digital mammography: associations between tabár and birads tissue compositions,” in Computer Analysis of Images and Patterns, vol. 8048 of Lecture Notes in Computer Science, pp. 386–393, Springer, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  79. J. J. Heine, K. Cao, D. E. Rollison, G. Tiffenberg, and J. A. Thomas, “A quantitative description of the percentage of breast density measurement using full-field digital mammography,” Academic Radiology, vol. 18, no. 5, pp. 556–564, 2011. View at Publisher · View at Google Scholar · View at Scopus
  80. O. Alonzo-Proulx, R. A. Jong, and M. J. Yaffe, “Volumetric breast density characteristics as determined from digital mammograms,” Physics in Medicine and Biology, vol. 57, no. 22, pp. 7443–7457, 2012. View at Publisher · View at Google Scholar · View at Scopus
  81. S. Ourselin, M. A. Styner, S. Malkov, K. Kerlikowske, and J. Shepherd, “Automated volumetric breast density derived by shape and appearance modeling,” in Medical Imaging, vol. 9034 of Proceedings of SPIE, pp. 90342T–90342T–7, San Diego, California, USA, March 2014. View at Publisher · View at Google Scholar
  82. K. Hartman, R. Highnam, R. Warren, and V. Jackson, “Volumetric assessment of breast tissue composition from FFDM images,” in Digital Mammography, vol. 5116 of Lecture Notes in Computer Science, pp. 33–39, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  83. R. Highnam, M. Brady, M. J. Yaffe, N. Karssemeijer, and J. Harvey, “Robust breast composition measurement—Volpara,” in Digital Mammography, vol. 6136 of Lecture Notes in Computer Science, pp. 342–349, Springer, Berlin, Germany, 2010. View at Publisher · View at Google Scholar
  84. A. Gubern-Mérida, M. Kallenberg, B. Platel et al., “Volumetric breast density estimation from full-field digital mammograms: a validation study,” PLoS ONE, vol. 9, no. 1, Article ID e85952, 2014. View at Publisher · View at Google Scholar
  85. J. N. Wolfe, A. F. Saftlas, and M. Salane, “Mammographic parenchymal patterns and quantitative evaluation of mammographic densities: a case-control study,” The American Journal of Roentgenology, vol. 148, no. 6, pp. 1087–1092, 1987. View at Publisher · View at Google Scholar · View at Scopus
  86. M. J. Yaffe, “Measurement of mammographic density,” Breast Cancer Research, vol. 10, no. 3, article 209, 2008. View at Publisher · View at Google Scholar · View at Scopus
  87. M. Nielsen, G. Karemore, M. Loog et al., “A novel and automatic mammographic texture resemblance marker is an independent risk factor for breast cancer,” Cancer Epidemiology, vol. 35, no. 4, pp. 381–387, 2011. View at Publisher · View at Google Scholar · View at Scopus
  88. C. Byrne, C. Schairer, J. N. Wolfe et al., “Mammographic features and breast cancer risk: effects with time, age, and menopause status,” Journal of the National Cancer Institute, vol. 87, no. 21, pp. 1622–1629, 1995. View at Publisher · View at Google Scholar · View at Scopus
  89. A. F. Saftlas, R. N. Hoover, L. A. Brinton et al., “Mammographic densities and risk of breast cancer,” Cancer, vol. 67, no. 11, pp. 2833–2838, 1991. View at Publisher · View at Google Scholar · View at Scopus
  90. K. Taylor, P. Britton, S. O'Keeffe, and M. G. Wallis, “Quantification of the UK 5-point breast imaging classification and mapping to BI-RADS to facilitate comparison with international literature,” The British Journal of Radiology, vol. 84, no. 1007, pp. 1005–1010, 2011. View at Publisher · View at Google Scholar · View at Scopus
  91. J. Brisson, C. Diorio, and B. Mâsse, “Wolfe's parenchymal pattern and percentage of the breast with mammographic densities: redundant or complementary classifications?” Cancer Epidemiology Biomarkers and Prevention, vol. 12, no. 8, pp. 728–732, 2003. View at Google Scholar · View at Scopus
  92. American College of Radiology, Breast Imaging Reporting and Data System (BI-RADS), American College of Radiology, Reston, Va, USA, 2014.
  93. J. Suckling, J. Parker, D. Dance et al., “The mammographic images analysis society digital mammogram database,” in Proceedings of the 2nd International Workshop on Digital Mammography, pp. 375–378, 1994.
  94. W. He, M. Kibiro, A. Juette, E. R. E. Denton, and R. Zwiggelaar, “A revisit on correlation between Tabár and Birads based risk assessment schemes with full field digital mammography,” in Breast Imaging—12th International Workshop (IWDM '14), Gifu City, Japan, June-July 2014. Proceedings, vol. 8539 of Lecture Notes in Computer Science, pp. 327–333, Springer, 2014. View at Google Scholar
  95. A. M. Oza and N. F. Boyd, “Mammographic parenchymal patterns: a marker of breast cancer risk,” Epidemiologic Reviews, vol. 15, no. 1, pp. 196–208, 1993. View at Google Scholar · View at Scopus
  96. N. F. Boyd, G. A. Lockwood, J. W. Byng, D. L. Tritchler, and M. J. Yaffe, “Mammographic densities and breast cancer risk,” Cancer Epidemiology Biomarkers and Prevention, vol. 7, no. 12, pp. 1133–1144, 1998. View at Google Scholar · View at Scopus
  97. P. M. Vacek and B. M. Geller, “A prospective study of breast cancer risk using routine mammographic breast density measurements,” Cancer Epidemiology Biomarkers & Prevention, vol. 13, no. 5, pp. 715–722, 2004. View at Google Scholar · View at Scopus
  98. S. V. Sree, E. Y. Ng, R. U. Acharya, and O. Faust, “Breast imaging: a survey,” World Journal of Clinical Oncology, vol. 2, no. 4, pp. 171–178, 2011. View at Google Scholar
  99. E. L. Rosen, W. B. Eubank, and D. A. Mankoff, “FDG PET, PET/CT, and breast cancer imaging,” Radiographics, vol. 27, supplement 1, pp. S215–S229, 2007. View at Publisher · View at Google Scholar · View at Scopus
  100. S. Malur, S. Wurdinger, A. Moritz, W. Michels, and A. Schneider, “Comparison of written reports of mammography, sonography and magnetic resonance mammography for preoperative evaluation of breast lesions, with special emphasis on magnetic resonance mammography,” Breast Cancer Research, vol. 3, no. 1, pp. 55–60, 2001. View at Publisher · View at Google Scholar · View at Scopus
  101. V. V. Levenson, “Biomarkers for early detection of breast cancer: what, when, and where?” Biochimica et Biophysica Acta—General Subjects, vol. 1770, no. 6, pp. 847–856, 2007. View at Publisher · View at Google Scholar · View at Scopus
  102. W. A. Berg, L. Gutierrez, M. S. NessAiver et al., “Diagnostic accuracy of mammography, clinical examination, US, and MR imaging in preoperative assessment of breast cancer,” Radiology, vol. 233, no. 3, pp. 830–849, 2004. View at Publisher · View at Google Scholar · View at Scopus
  103. T. M. Kolb, J. Lichy, and J. H. Newhouse, “Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations,” Radiology, vol. 225, no. 1, pp. 165–175, 2002. View at Publisher · View at Google Scholar · View at Scopus
  104. H. Thornton, A. Edwards, and M. Baum, “Women need better information about routine mammography,” British Medical Journal, vol. 327, no. 7406, pp. 101–103, 2003. View at Publisher · View at Google Scholar · View at Scopus
  105. K. Kerlikowske, D. Grady, J. Barclay et al., “Variability and accuracy in mammographic interpretation using the American college of radiology breast imaging reporting and data system,” Journal of the National Cancer Institute, vol. 90, no. 23, pp. 1801–1809, 1998. View at Publisher · View at Google Scholar · View at Scopus
  106. M. B. I. Lobbes, J. P. M. Cleutjens, V. Lima Passos et al., “Density is in the eye of the beholder: visual versus semi-automated assessment of breast density on standard mammograms,” Insights into Imaging, vol. 3, no. 1, pp. 91–99, 2012. View at Publisher · View at Google Scholar · View at Scopus
  107. E. D. Pisano, R. E. Hendrick, M. J. Yaffe et al., “Diagnostic accuracy of digital versus film mammography: exploratory analysis of selected population subgroups in DMIST,” Radiology, vol. 246, no. 2, pp. 376–383, 2008. View at Publisher · View at Google Scholar · View at Scopus
  108. A. Fischmann, K. C. Siegmann, A. Wersebe, C. D. Claussen, and M. Müller-Schimpfle, “Comparison of full-field digital mammography and film-screen mammography: image quality and lesion detection,” British Journal of Radiology, vol. 78, no. 928, pp. 312–315, 2005. View at Publisher · View at Google Scholar · View at Scopus
  109. F. J. Gillbert, K. C. Young, S. M. Astley, P. Whelehan, and M. G. C. Gillan, Digital Breast Tomosynthesis, NHSBSP Publication, 2010.
  110. T. Wu, A. Stewart, M. Stanton et al., “Tomographic mammography using a limited number of lowdose cone-beam projection images,” Medical Physics, vol. 30, pp. 365–380, 2003. View at Google Scholar
  111. E. H. L. Mungutroy, J. M. Oduko, J. C. Cooke, and W. J. Formstone, Practical Evaluation of Hologic Selenia Dimensions Digital Breast Tomosynthesis System, NHSBSP Publication, 2014.
  112. J. Marshall, A. Kshirsagar, S. Narin, and N. Gkanatsios, “Will new technologies replace mammography CAD as we know it?” in Breast Imaging, vol. 8539 of Lecture Notes in Computer Science, pp. 30–37, Springer, Cham, Switzerland, 2014. View at Publisher · View at Google Scholar
  113. P. K. Saha, J. K. Udupa, and D. Odhner, “Scale-based fuzzy connected image segmentation: theory, algorithms, and validation,” Computer Vision and Image Understanding, vol. 77, no. 2, pp. 145–174, 2000. View at Publisher · View at Google Scholar · View at Scopus
  114. J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Recognition, vol. 19, no. 1, pp. 41–47, 1986. View at Publisher · View at Google Scholar · View at Scopus
  115. A. D. Brink and N. E. Pendock, “Minimum cross-entropy threshold selection,” Pattern Recognition, vol. 29, no. 1, pp. 179–188, 1996. View at Publisher · View at Google Scholar · View at Scopus
  116. J. Rissanen, “Modeling by shortest data description,” Automatica, vol. 14, no. 5, pp. 465–471, 1978. View at Publisher · View at Google Scholar · View at Scopus
  117. A. K. C. Wong and P. K. Sahoo, “Gray-level threshold selection method based on maximum entropy principle,” IEEE Transactions on Systems, Man and Cybernetics, vol. 19, no. 4, pp. 866–871, 1989. View at Publisher · View at Google Scholar · View at Scopus
  118. J. N. Kapur, “Twenty-five years of maximum-entropy principle,” Journal of Mathematical and Physical Sciences, vol. 17, no. 2, pp. 103–156, 1983. View at Google Scholar · View at MathSciNet
  119. K. E. Martin, M. A. Helvie, C. Zhou et al., “Mammographic density measured with quantitative computer-aided method: comparison with radiologists' estimates and BI-RADS categories,” Radiology, vol. 240, no. 3, pp. 656–665, 2006. View at Publisher · View at Google Scholar · View at Scopus
  120. J. MacQueen, “Some methods of classification and analysis of multivariate observations,” in Proceedings of the Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297, 1967.
  121. R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973. View at Publisher · View at Google Scholar · View at Scopus
  122. K. Zhang and J. T. Kwok, “Clustered Nystrm method for large scale manifold learning and dimension reduction,” IEEE Transactions on Neural Networks, vol. 21, no. 10, pp. 1576–1587, 2010. View at Publisher · View at Google Scholar · View at Scopus
  123. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, New York, NY, USA, 2013.
  124. M. Heath, K. Bowyer, D. Kopans et al., “Current status of the digital database for screening mammography,” in Proceedings of the 4th International Workshop on Digital Mammography, pp. 457–460, Nijmegen, The Netherlands, June 1998.
  125. J. Shi and J. Malik, “Normalized cuts and image segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888–905, 2000. View at Publisher · View at Google Scholar · View at Scopus
  126. D. Comaniciu and P. Meer, “Mean shift: a robust approach toward feature space analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603–619, 2002. View at Publisher · View at Google Scholar · View at Scopus
  127. D. Raba, J. Martí, R. Martí, and M. Peracaula, “Breast mammography asymmetry estimation based on fractal and texture analysis,” in Computed Aided Radiology and Surgery (CARS '05), 2005.
  128. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, “Eigenfaces vs. fisherfaces: recognition using class specific linear projection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, 1997. View at Publisher · View at Google Scholar · View at Scopus
  129. D. P. Feldman and J. P. Crutchfield, “Structural information in two-dimensional patterns: entropy convergence and excess entropy,” Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, vol. 67, no. 5, Article ID 051104, 2003. View at Publisher · View at Google Scholar · View at MathSciNet
  130. E. Riboli, K. J. Hunt, N. Slimani et al., “European prospective investigation into cancer and nutrition (EPIC): study populations and data collection,” Public Health Nutrition, vol. 5, no. 6, pp. 1113–1124, 2002. View at Publisher · View at Google Scholar · View at Scopus
  131. A. P. Dempster, N. M. Laird, and D. B. Rubin, “Maximum likelihood from incomplete data via the EM algorithm,” Journal of the Royal Statistical Society, Series B: Methodological, vol. 39, no. 1, pp. 1–38, 1977. View at Google Scholar · View at MathSciNet
  132. J. Rissanen, “Modeling by shortest data description,” Automatica, vol. 14, no. 5, pp. 465–471, 1978. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  133. Y. Rubner, C. Tomasi, and L. J. Guibas, “Metric for distributions with applications to image databases,” in Proceedings of the IEEE 6th International Conference on Computer Vision, pp. 59–66, January 1998. View at Scopus
  134. R. C. Eberhart and Y. Shi, “Comparison between genetic algorithms and particle swarm optimization,” in Evolutionary Programming VII, vol. 1447 of Lecture Notes in Computer Science, pp. 611–616, Springer, Berlin, Germany, 1998. View at Publisher · View at Google Scholar
  135. X. Yao, Y. Liu, and G. Lin, “Evolutionary programming made faster,” IEEE Transactions on Evolutionary Computation, vol. 3, no. 2, pp. 82–102, 1999. View at Publisher · View at Google Scholar · View at Scopus
  136. J. Serra, Image Analysis and Mathematical Morphology, Academic Press, New York, NY, USA, 1984. View at MathSciNet
  137. K. I. Laws, “Textured image segmentation,” Tech. Rep. 940, Image Processing Institute, University of Southern California, Los Angeles, Calif, USA, 1980. View at Google Scholar
  138. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, The Morgan Kaufmann Series in Representation and Reasoning, Morgan Kaufmann, San Mateo, Calif, USA, 1988. View at MathSciNet
  139. D. J. Lewis, D. G. Corr, C. R. Gent, and C. P. Sheppard, “The use of multiple linked self-organizing artificial neural networks for classification of remotely sensed images,” in Proceedings of the Satellite Symposia 1 & 2: Navigation and Mobile Communication and Image Processing ESA-IY2, pp. 333–345, 1992.
  140. S. Petroudi, T. Kadir, and M. Brady, “Automatic classification of mammographic parenchymal patterns: a statistical approach,” Engineering in Medicine and Biology Society, vol. 1, pp. 798–801, 2003. View at Google Scholar
  141. P. Brault and A. Mohammad-Djafari, “Bayesian segmentation and motion estimation in video sequences using a Markov-Potts model,” in Proceedings of the 5th WSEAS International Conference on Applied Mathematics (Math '04), 2004.
  142. Y. Zhang, M. Brady, and S. Smith, “Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm,” IEEE Transactions on Medical Imaging, vol. 20, no. 1, pp. 45–57, 2001. View at Publisher · View at Google Scholar · View at Scopus
  143. M. Varma and A. Zisserman, “Classifying images of materials: achieving viewpoint and illumination independence,” in Computer Vision—ECCV 2002, vol. 2352 of Lecture Notes in Computer Science, pp. 255–271, Springer, Berlin, Germany, 2002. View at Publisher · View at Google Scholar
  144. M. A. Turk and A. P. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991. View at Publisher · View at Google Scholar · View at Scopus
  145. M. Pietikainen, “Image analysis with local binary patterns,” in Image Analysis, vol. 3540 of Lecture Notes in Computer Science, pp. 115–118, Springer, Berlin, Germany, 2005. View at Publisher · View at Google Scholar
  146. S. M. Smith and J. M. Brady, “SUSAN—a new approach to low level image processing,” International Journal of Computer Vision, vol. 23, no. 1, pp. 45–78, 1997. View at Publisher · View at Google Scholar · View at Scopus
  147. R. Hupse and N. Karssemeijer, “The effect of feature selection methods on computer-aided detection of masses in mammograms,” Physics in Medicine and Biology, vol. 55, no. 10, pp. 2893–2904, 2010. View at Publisher · View at Google Scholar · View at Scopus
  148. C. A. Schneider, W. S. Rasband, and K. W. Eliceiri, “NIH Image to ImageJ: 25 years of image analysis,” Nature Methods, vol. 9, no. 7, pp. 671–675, 2012. View at Publisher · View at Google Scholar · View at Scopus
  149. Z. Lao and Z. Huo, “Quantitative assessment of breast dense tissue on mammograms,” in Proceedings of the IEEE International Conference on Image Processing (ICIP '09), pp. 2605–2608, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  150. W. He, I. Muhimmah, E. R. E. Denton, and R. Zwiggelaar, “Mammographic segmentation based on texture modelling of Tabár mammographic building blocks,” in Digital Mammography, vol. 5116 of Lecture Notes in Computer Science, pp. 17–24, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  151. M. Tuceryan, “Moment-based texture segmentation,” Pattern Recognition Letters, vol. 15, no. 7, pp. 659–668, 1994. View at Publisher · View at Google Scholar · View at Scopus
  152. M. Tortajada, A. Oliver, R. Martí et al., “Breast peripheral area correction in digital mammograms,” Computers in Biology and Medicine, vol. 50, pp. 32–40, 2014. View at Publisher · View at Google Scholar · View at Scopus
  153. W. He and R. Zwiggelaar, “Image classification: a novel texture signature approach,” in Proceedings of the 17th IEEE International Conference on Image Processing (ICIP '10), pp. 2725–2728, IEEE, Hong Kong, China, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  154. R. P. Highnam, M. Jeffreys, V. McCormack, R. Warren, G. D. Smith, and M. Brady, “Comparing measurements of breast density,” Physics in Medicine and Biology, vol. 52, no. 19, pp. 5881–5895, 2007. View at Publisher · View at Google Scholar · View at Scopus
  155. D. B. Kopans, “Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk,” Radiology, vol. 246, no. 2, pp. 348–353, 2008. View at Publisher · View at Google Scholar · View at Scopus
  156. C. M. Vachon, C. H. van Gils, T. A. Sellers et al., “Mammographic density, breast cancer risk and risk prediction,” Breast Cancer Research, vol. 9, no. 6, article 217, 2007. View at Publisher · View at Google Scholar · View at Scopus
  157. R. Highnam and M. Brady, Mammographic Image Analysis, Kluwer Academic, New York, NY, USA, 1999.
  158. J. Kaufhold, J. A. Thomas, J. W. Eberhard, C. E. Galbo, and D. E. González Trotter, “A calibration approach to glandular tissue composition estimation in digital mammography,” Medical Physics, vol. 29, no. 8, pp. 1867–1880, 2002. View at Publisher · View at Google Scholar · View at Scopus
  159. O. Pawluczyk, B. J. Augustine, M. J. Yaffe et al., “A volumetric method for estimation of breast density on digitized screen-film mammograms,” Medical Physics, vol. 30, no. 3, pp. 352–364, 2003. View at Publisher · View at Google Scholar · View at Scopus
  160. A. H. Tyson, G. E. Mawdsley, and M. J. Yaffe, “Measurement of compressed breast thickness by optical stereoscopic photogrammetry,” Medical Physics, vol. 36, no. 2, pp. 569–576, 2009. View at Publisher · View at Google Scholar · View at Scopus
  161. G. E. Mawdsley, A. H. Tyson, C. L. Peressotti, R. A. Jong, and M. J. Yaffe, “Accurate estimation of compressed breast thickness in mammography,” Medical Physics, vol. 36, no. 2, pp. 577–586, 2009. View at Publisher · View at Google Scholar · View at Scopus
  162. O. Alonzo-Proulx, N. Packard, J. M. Boone et al., “Validation of a method for measuring the volumetric breast density from digital mammograms,” Physics in Medicine and Biology, vol. 55, no. 11, pp. 3027–3044, 2010. View at Publisher · View at Google Scholar · View at Scopus
  163. A. Hufton, S. M. Astley, T. Marchant, and H. Patel, “A method for the quantification of dense breast tissue from digitised mammograms,” in Proceedings of the International Workshop on Digital Mammography, pp. 430–435, 2004.
  164. M. Berks, J. Diffey, A. Hufton, and S. Astley, “Feasibility and acceptability of stepwedge-based density measurement,” in Proceedings of the International Workshop on Digital Mammography, pp. 355–361, 2006.
  165. J. A. Shepherd, L. Herve, J. Landau, B. Fan, K. Kerlikowske, and S. R. Cummings, “Novel use of single X-Ray absorptiometry for measuring breast density,” Technology in Cancer Research & Treatment, vol. 4, no. 2, pp. 173–182, 2005. View at Publisher · View at Google Scholar · View at Scopus
  166. S. Malkov, J. Wang, K. Kerlikowske, S. R. Cummings, and J. A. Shepherd, “Single X-ray absorptiometry method for the quantitative mammographic measure of fibroglandular tissue volume,” Medical Physics, vol. 36, no. 12, pp. 5525–5536, 2009. View at Publisher · View at Google Scholar · View at Scopus
  167. J. A. Shepherd, K. Kerlikowske, L. Ma et al., “Volume of mammographic density and risk of breast cancer,” Cancer Epidemiology Biomarkers & Prevention, vol. 20, no. 7, pp. 1473–1482, 2011. View at Publisher · View at Google Scholar · View at Scopus
  168. S. Malkov, J. Wang, F. Duewer, and J. A. Shepherd, “A calibration approach for single-energy x-ray absorptiometry method to provide absolute breast tissue composition accuracy for the long term,” in Breast Imaging, Lecture Notes in Computer Science, pp. 769–774, Springer, 2012. View at Google Scholar
  169. R. Highnam, X. Pan, R. Warren, M. Jeffreys, G. Davey Smith, and M. Brady, “Breast composition measurements using retrospective standard mammogram form (SMF),” Physics in Medicine and Biology, vol. 51, no. 11, pp. 2695–2713, 2006. View at Publisher · View at Google Scholar · View at Scopus
  170. S. Petroudi, K. Marias, R. English, R. Adams, and M. Brady, “Classification of mammographic patterns using area measurements and the standard mammogram form (SMF),” in Medical Image Understanding and Analysis, pp. 197–200, 2002. View at Google Scholar
  171. K. Marias, S. Petroudi, R. English, R. Adams, and M. Brady, “Subjective and computer-based characterisation of mammographic patterns,” in Proceedings of the International Conference on Digital Mammography, pp. 552–557, 2002.
  172. K. Marias, C. Behrenbruch, R. Highnam, S. Parbhoo, A. Seifalian, and M. Brady, “A mammographic image analysis method to detect and measure changes in breast density,” European Journal of Radiology, vol. 52, no. 3, pp. 276–282, 2004. View at Publisher · View at Google Scholar · View at Scopus
  173. V. A. McCormack, R. Highnam, N. Perry, and I. D. S. Silva, “Comparison of a new and existing method of mammographic density measurement: intramethod reliability and associations with known risk factors,” Cancer Epidemiology, Biomarkers & Prevention, vol. 16, no. 6, pp. 1148–1154, 2007. View at Publisher · View at Google Scholar · View at Scopus
  174. J. Ding, R. Warren, I. Warsi et al., “Evaluating the effectiveness of using standard mammogram form to predict breast cancer risk: case-control study,” Cancer Epidemiology Biomarkers & Prevention, vol. 17, no. 5, pp. 1074–1081, 2008. View at Publisher · View at Google Scholar · View at Scopus
  175. S. van Engeland, P. R. Snoeren, H. Huisman, C. Boetes, and N. Karssemeijer, “Volumetric breast density estimation from full-field digital mammograms,” IEEE Transactions on Medical Imaging, vol. 25, no. 3, pp. 273–282, 2006. View at Publisher · View at Google Scholar · View at Scopus
  176. S. Lau, K. H. Ng, and Y. F. A. Aziz, “Are volumetric breast density measurements robust enough for routine clinical use?” in Proceedings of the European Congress of Radiology.
  177. J. Wang, A. Azziz, B. Fan et al., “Agreement of mammographic measures of volumetric breast density to MRI,” PLoS ONE, vol. 8, no. 12, Article ID e81653, 2013. View at Publisher · View at Google Scholar · View at Scopus
  178. M. Jeffreys, J. Harvey, and R. Highnam, “Comparing a new volumetric breast density method (Volpara) to Cumulus,” in Digital Mammography, vol. 6136 of Lecture Notes in Computer Science, pp. 408–413, Springer, Berlin, Germany, 2010. View at Google Scholar
  179. L. Beattie, E. Harkness, M. Bydder et al., “Factors affecting agreement between breast density assessment using volumetric methods and visual analogue scales,” in Breast Imaging, vol. 8539 of Lecture Notes in Computer Science, pp. 80–87, Springer, Cham, Switzerland, 2014. View at Publisher · View at Google Scholar
  180. J. S. Brand, K. Czene, J. A. Shepherd et al., “Automated measurement of volumetric mammographic density: a tool for widespread breast cancer risk assessment,” Cancer Epidemiology Biomarkers Prevention, vol. 23, no. 9, pp. 1764–1772, 2014. View at Google Scholar
  181. D. Kontos, R. Berger, P. R. Bakic, and A. D. A. Maidment, “Breast tissue classification in digital breast tomosynthesis images using texture features: a feasibility study,” in Medical Imaging 2009: Computer-Aided Diagnosis, Proceedings of SPIE, Lake Buena Vista, Fla, USA, February 2009. View at Publisher · View at Google Scholar · View at Scopus
  182. D. Kontos, P. R. Bakic, A. B. Troxel, E. F. Conant, and A. D. A. Maidment, “Digital breast tomosynthesis parenchymal texture analysis for breast cancer risk estimation: a preliminary study,” in Digital Mammography, vol. 5116 of Lecture Notes in Computer Science, pp. 681–688, Springer, Berlin, Germany, 2008. View at Publisher · View at Google Scholar
  183. E. M. S. Pertuz, S. P. Weinstein, E. F. Conant, and D. Kontos, “Fully automated volumetric breast density estimation from digital breast tomosynthesis images: multi-modality comparison with digital mammography and breast MRI,” in Proceedings of the Radiological Society of North America—Scientific Assembly and Annual Meeting, 2014.
  184. C. M. Shafer, V. L. Seewaldt, and J. Y. Lo, “Validation of a 3D hidden-Markov model for breast tissue segmentation and density estimation from MR and tomosynthesis images,” in Biomedical Sciences and Engineering Conference (BSEC '11), pp. 1–4, March 2011. View at Publisher · View at Google Scholar · View at Scopus
  185. C. M. Shafer, V. L. Seewaldt, and J. Y. Lo, “Segmentation of adipose and glandular tissue for breast tomosynthesis imaging using a 3D hidden-Markov model trained on breast MRIs,” in Medical Imaging 2011: Physics of Medical Imaging, vol. 7961 of Proceedings of SPIE, February 2011. View at Publisher · View at Google Scholar · View at Scopus
  186. H. D. Li, M. Kallergi, L. P. Clarke, V. K. Jain, and R. A. Clark, “Markov random field for tumor detection in digital mammography,” IEEE Transactions on Medical Imaging, vol. 14, no. 3, pp. 565–576, 1995. View at Publisher · View at Google Scholar · View at Scopus
  187. J. A. Harvey and V. E. Bovbjerg, “Quantitative assessment of mammographic breast density: relationship with breast cancer risk,” Radiology, vol. 230, no. 1, pp. 29–41, 2004. View at Publisher · View at Google Scholar · View at Scopus
  188. I. C. Moreira, I. Amaral, I. Domingues, A. Cardoso, M. J. Cardoso, and J. S. Cardoso, “INbreast: toward a full-field digital mammographic database,” Academic Radiology, vol. 19, no. 2, pp. 236–248, 2012. View at Publisher · View at Google Scholar · View at Scopus
  189. W. He, E. R. E. Denton, and R. Zwiggelaar, “A study on mammographic image modelling and classification using multiple databases,” in Breast Imaging, vol. 8539 of Lecture Notes in Computer Science, pp. 696–701, Springer, 2014. View at Publisher · View at Google Scholar
  190. H. F. Boehm, T. Schneider, S. M. Buhmann-Kirchhoff et al., “Automated classification of breast parenchymal density: topologic analysis of X-ray attenuation patterns depicted with digital mammography,” American Journal of Roentgenology, vol. 191, no. 6, pp. W275–W282, 2008. View at Publisher · View at Google Scholar · View at Scopus
  191. R. T. Chlebowski, L. H. Kuller, R. L. Prentice et al., “Breast cancer after use of estrogen plus progestin in postmenopausal women,” The New England Journal of Medicine, vol. 360, no. 6, pp. 573–587, 2009. View at Publisher · View at Google Scholar · View at Scopus
  192. C. M. Vachon, V. S. Pankratz, C. G. Scott et al., “Longitudinal trends in mammographic percent density and breast cancer risk,” Cancer Epidemiology Biomarkers and Prevention, vol. 16, no. 5, pp. 921–928, 2007. View at Publisher · View at Google Scholar · View at Scopus
  193. N. F. Boyd, L. J. Martin, Q. Li et al., “Mammographic density as a surrogate marker for the effects of hormone therapy on risk of breast cancer,” Cancer Epidemiology Biomarkers & Prevention, vol. 15, no. 5, pp. 961–966, 2006. View at Publisher · View at Google Scholar · View at Scopus
  194. B. Threatt, J. M. Norbeck, N. S. Ullman, R. Kummer, and P. Roselle, “Association between mammographic parenchymal pattern classification and incidence of breast cancer,” Cancer, vol. 45, no. 10, pp. 2550–2556, 1980. View at Google Scholar · View at Scopus
  195. D. B. Thomas, R. A. Carter, W. H. Bush Jr. et al., “Risk of subsequent breast cancer in relation to characteristics of screening mammograms from women less than 50 years of age,” Cancer Epidemiology Biomarkers & Prevention, vol. 11, no. 6, pp. 565–571, 2002. View at Google Scholar · View at Scopus
  196. A. Manduca, M. J. Carston, J. J. Heine et al., “Texture features from mammographic images and risk of breast cancer,” Cancer Epidemiology, Biomarkers & Prevention, vol. 18, no. 3, pp. 837–845, 2009. View at Publisher · View at Google Scholar · View at Scopus
  197. J. Couzin, “Dissecting a hidden breast cancer risk,” Science, vol. 309, no. 5741, pp. 1664–1666, 2005. View at Publisher · View at Google Scholar · View at Scopus
  198. G. Torres-Mejía, B. de Stavola, D. S. Allen et al., “Mammographic features and subsequent risk of breast cancer: a comparison of qualitative and quantitative evaluations in the guernsey prospective studies,” Cancer Epidemiology Biomarkers & Prevention, vol. 14, no. 5, pp. 1052–1059, 2005. View at Publisher · View at Google Scholar · View at Scopus
  199. H. Li, M. L. Giger, O. I. Olopade, and L. Lan, “Fractal analysis of mammographic parenchymal patterns in breast cancer risk assessment,” Academic Radiology, vol. 14, no. 5, pp. 513–521, 2007. View at Publisher · View at Google Scholar · View at Scopus
  200. B. T. Nicholson, A. P. LoRusso, M. Smolkin, V. E. Bovbjerg, G. R. Petroni, and J. A. Harvey, “Accuracy of Assigned BI-RADS Breast Density Category Definitions,” Academic Radiology, vol. 13, no. 9, pp. 1143–1149, 2006. View at Publisher · View at Google Scholar · View at Scopus