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Breast density measurement methods on mammograms: a review

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Abstract

In recent years, due to the high correlation between breast cancer and breast density, the quantitative and qualitative analysis of breast density has attracted wide attention from researchers. To provide approaches for researchers who are eager for breast density measurement through computer-aided diagnosis (CAD) manner, we survey related works in the past 2 decades. In this work, we review the methods of breast density analysis in terms of qualitative and quantitative measurements based on image analysis, respectively. Various applications of these methods were mentioned, and underlying difficulties, limitations, merits, and disadvantages were discussed in applying these methods. The researchers will find it helpful to choose and use the appropriate model for a better application.

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Notes

  1. http://peipa.essex.ac.uk/info/mias.html.

  2. medicalresearch@inescporto.pt

  3. http://www.eng.usf.edu/cvprg/Mammography/Database.html.

  4. http://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM.

  5. https://www.cancerimagingarchive.net/

References

  1. Eisemann, N., Waldmann, A., Katalinic, A.: Epidemiology of breast cancer-current figures and trends. Geburtshilfe Und Frauenheilkunde 73(2), 130 (2013)

    Google Scholar 

  2. Desantis, C., Ma, J., Gaudet, M.M., Newman, L.A., Miller, K.D., Sauer, A.G., Jemal, A., Siegel, R.L.: CA: breast cancer statistics, 2019. Cancer J. Clin. 69(6), 438 (2019)

    Google Scholar 

  3. Boyd, N.F., Lockwood, G.A., Martin, L.J., Knight, J.A., Jong, R.A., Fishell, E., Byng, J.W., Yaffe, M.J., Tritchler, D.L.: Mammographic densities and risk of breast cancer among subjects with a family history of this disease. J. Natl. Cancer Inst. 91(16), 1404 (1999)

    Google Scholar 

  4. Ursin, G., Ma, H., Wu, A.H., Bernstein, L., Salane, M., Parisky, Y.R., Astrahan, M., Siozon, C.C., Pike, M.C.: Mammographic density and breast cancer in three ethnic groups. Cancer Epidemiol. Prevent. Biomark. 12(4), 332 (2003)

    Google Scholar 

  5. Boyd, N.F., Rommens, J.M., Vogt, K., Lee, V., Hopper, J.L., Yaffe, M.J., Paterson, A.D.: Mammographic breast density as an intermediate phenotype for breast cancer. Lancet Oncol. 6(10), 798 (2005)

    Google Scholar 

  6. Mccormack, V., Silva, I.D.S.: Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol. Biomark. Prevent. 15(6), 1159 (2006)

    Google Scholar 

  7. Harvey, J.A., Bovbjerg, V.E.: Quantitative assessment of mammographic breast density: relationship with breast cancer risk. Radiology 230(1), 29 (2004)

    Google Scholar 

  8. Sak, M.A., Littrup, P.J., Duric, N., Mullooly, M., Sherman, M.E., Gierach, G.L.: Current and future methods for measuring breast density: a brief comparative review. Breast Cancer Manag. 4(4), 209 (2015)

    Google Scholar 

  9. Alomaim, W., O’Leary, D., Ryan, J., Rainford, L., Evanoff, M., Foley, S.: Subjective versus quantitative methods of assessing breast density. Diagnostics 10(5), 331 (2020)

    Google Scholar 

  10. Saffari, N., Rashwan, H.A., Abdel-Nasser, M., Kumar Singh, V., Arenas, M., Mangina, E., Herrera, B., Puig, D.: Fully automated breast density segmentation and classification using deep learning. Diagnostics 10(11), 988 (2020)

    Google Scholar 

  11. Wolfe, J.N.: Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37(5), 2486 (1976)

    Google Scholar 

  12. Gram, I.T., Funkhouser, E., Tabár, L.: The Tabar classification of mammographic parenchymal patterns. Eur. J. Radiol. 24(2), 131 (1997)

    Google Scholar 

  13. Oliver, A., Freixenet, J., Zwiggelaar, R.: Automatic classification of breast density. In: IEEE International Conference on Image Processing 2005, vol. 2 (IEEE, 2005), vol. 2, pp. II–1258

  14. Mousa, D.A., Ryan, E., Mello-Thoms, C., Brennan, P.: What effect does mammographic breast density have on lesion detection in digital mammography. Clin. Radiol. 69(4), 333 (2014)

    Google Scholar 

  15. Gram, I.T., Bremnes, Y., Ursin, G., Maskarinec, G., Bjurstam, N., Lund, E.: Percentage density, Wolfe’s and Tabar’s mammographic patterns: agreement and association with risk factors for breast cancer. Breast Cancer Res. 7(5), R854 (2005)

    Google Scholar 

  16. Wolfe, J.N.: Breast parenchymal patterns and their changes with age. Radiology 121(3), 545 (1976)

    Google Scholar 

  17. Liberman, L., Menell, J.H.: Breast imaging reporting and data system (BI-RADS). Radiol. Clin. 40(3), 409 (2002)

    Google Scholar 

  18. Ekpo, E.U., Ujong, U.P., Mello-Thoms, C., McEntee, M.F.: Assessment of interradiologist agreement regarding mammographic breast density classification using the fifth edition of the BI-RADS atlas. Am. J. Roentgenol. 206(5), 1119 (2016)

  19. Tlusty, T., Amit, G., Ben-Ari, R.: Unsupervised clustering of mammograms for outlier detection and breast density estimation. In: International Conference on Pattern Recognition (2018)

  20. Zhou, C., Chan, H.P., Petrick, N., Helvie, M.A., Goodsitt, M.M., Sahiner, B., Hadjiiski, L.M.: Computerized image analysis: estimation of breast density on mammograms. Med. Phys. 28(6), 1056 (2001)

    Google Scholar 

  21. Muštra, M.G., Delač, M.K.: Feature selection for automatic breast density classification. Int. Symp. Elmar

  22. Kumar, I., Bhadauria, H.S., Virmani, J., Thakur, S.: A hybrid hierarchical framework for classification of breast density using digitized film screen mammograms. Multimed. Tools Appl. (2017)

  23. Zeng, Y.C.: Mammogram Density Classification using Double Support Vector Machines. In: 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) (IEEE, 2018), pp. 547–550

  24. Bovis, K., Singh, S.: Classification of mammographic breast density using a combined classifier paradigm. In: 4th international workshop on digital mammography, pp. 177–180 (2002)

  25. Tzikopoulos, S., Georgiou, H., Mavroforakis, M., Theodoridis, S.: A fully automated scheme for breast density estimation and asymmetry detection of mammograms. In: 2009 17th European Signal Processing Conference (IEEE, 2009), pp. 1869–1873

  26. Chen, Z., Denton, E., Zwiggelaar, R.: Local feature based mammographic tissue pattern modelling and breast density classification. In: 2011 4th International Conference on Biomedical Engineering and Informatics (BMEI), vol. 1 (IEEE, 2011), vol. 1, pp. 351–355

  27. Qi, Y., Yang, Z., Lei, J., Lian, J., Liu, J., Feng, W., Ma, Y.: Morph_SPCNN model and its application in breast density segmentation. Multimed. Tools Appl. pp. 1–25 (2020)

  28. Alexander, C., Cristina, R., Ilaria, V., De Martini, M.E.: Determination of mammographic breast density using a deep convolutional neural network. Br. J. Radiol. (2018)

  29. Mohamed, A.A. , Luo, Y., Peng,H. , Jankowitz, R .C., Wu, S.: Understanding Clinical Mammographic Breast Density Assessment: a Deep Learning Perspective, Journal of Digital Imaging (2017)

  30. Lehman, C.D., Yala, A., Schuster, T., Dontchos, B., Bahl, M., Swanson, K., Barzilay, R.: Mammographic breast density assessment using deep learning: clinical implementation. Radiology (2018)

  31. Mohamed, A.A., Berg, W.A., Peng, H., Luo, Y., Jankowitz, R.C., Wu, S.: A deep learning method for classifying mammographic breast density categories. Med. Phys. (2017)

  32. Paul, H.Y., Lin, A., Wei, J., Alice, C.Y., Sair, H.I., Hui, F.K., Hager, G.D., Harvey, S.C.: Deep-learning-based semantic labeling for 2d mammography and comparison of complexity for machine learning tasks. J. Digit. Imaging 32(4), 565 (2019)

    Google Scholar 

  33. Gandomkar, Z., Suleiman, M.E., Demchig, D., Brennan, P.C., McEntee, M.F.: BI-RADS density categorization using deep neural networks. In: Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, vol. 10952 (International Society for Optics and Photonics, 2019), vol. 10952, p. 109520N

  34. Wang, R., Ma, Y., Sun, W., Guo, Y., Wang, W., Qi, Y., Gong, X.: Multi-level nested pyramid network for mass segmentation in mammograms. Neurocomputing 363, 313 (2019)

    Google Scholar 

  35. Kaiser, N., Fieselmann, A., Vesal, S., Ravikumar, N., Ritschl, L., Kappler, S., Maier, A.: Mammographic breast density classification using a deep neural network: assessment based on inter-observer variability. In: Medical Imaging 2019: Image Perception, Observer Performance, and Technology Assessment, vol. 10952 (International Society for Optics and Photonics, 2019), vol. 10952, p. 109520O

  36. Tardy, M., Scheffer, B., Mateus, D.: Breast density quantification using weakly annotated dataset. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI) (2019)

  37. Gardezi, J, Muhammad, A.I., Faye, I., Meriaudeau, F.: Mammogram classification using deep learning features. In: IEEE International Conference on Signal & Image Processing Applications (2017)

  38. Wang, R., Guo, Y., Wang, W., Ma, Y.: Bi-ResNet: fully automated classification of unregistered contralateral mammograms. In: International Conference on Artificial Neural Networks (Springer, 2019), pp. 273–283

  39. Matsuyama, E., Takehara, M., Tsai, D.Y.: Using a wavelet-based and fine-tuned convolutional neural network for classification of breast density in mammographic images. Open J. Med. Imaging 10(1), 17 (2020)

    Google Scholar 

  40. Elshinawy, M., Badawy, A., Abdelmageed, W., Chouikha, M.: Effect of breast density in selecting features for normal mammogram detection. In: Proceedings pp. 141–147 (2011)

  41. Kumar, I., B.H. S., Virmani, J., Thakur, S.: A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybern. Biomed. Eng. 37(1), 217 (2017)

  42. Nithya, R., Santhi, B.: Application of texture analysis method for mammogram density classification. J. Instrument. 12(07), P07009 (2017)

    Google Scholar 

  43. de Oliveira, J.E.E., de Albuquerque Araújo, A.: Deserno, T.M.: Content-based image retrieval applied to BI-RADS tissue classification in screening mammography. World J. Radiol. 3(1), 24 (2011)

  44. Gong, X., Yang, Z., Wang, D., Qi, Y., Ma, Y.: Breast density analysis based on glandular tissue segmentation and mixed feature extraction, Multimed. Tools Appl. (5) (2019)

  45. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision & Pattern Recognition (2005)

  46. Trivizakis, E., Ioannidis, G., Melissianos, V., Papadakis, G., Marias, K.: A novel deep learning architecture outperforming ’off-the-shelf’ transfer learning and feature-based methods in the automated assessment of mammographic breast density. Oncol. Rep. (2019)

  47. Li, L., Jian, W., Kai, H.: Breast density classification using histogram moments of multiple resolution mammograms. In: International Conference on Biomedical Engineering & Informatics (2010)

  48. Petroudi, S., Constantinou, I., Tziakouri, C., Pattichis, M., Pattichis, C.: Investigation of AM-FM methods for mammographic breast density classification. In: IEEE International Conference on Bioinformatics & Bioengineering (2013)

  49. Constantinou, I., Pattichis, M., Tziakouri, C., Pattichis, C., Nicosia, C.: Multiscale AM-FM models and instantaneous amplitude evaluation for mammographic density classification. In: MIUA (2014)

  50. De Siqueira, F.R., Schwartz, W.R., Pedrini, H.: Multi-scale gray level co-occurrence matrices for texture description. Neurocomputing 120, 336 (2013)

    Google Scholar 

  51. Tortajada, M. Oliver, A., Martí, R., Vilagran, M., Freixenet, J.: Adapting breast density classification from digitized to full-field digital mammograms. IWDM (2012)

  52. Virmani, J., et al.: Comparison of CAD systems for three class breast tissue density classification using mammographic images. In: Medical imaging in clinical applications (Springer, 2016), pp. 107–130

  53. Pisano, E.D., Zong, S., Hemminger, B.M., DeLuca, M., Johnston, R.E., Muller, K., Braeuning, M.P., Pizer, S.M.: Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J. Digit. Imaging 11(4), 193 (1998)

    Google Scholar 

  54. Laws, K.I.: Rapid texture identification. Spie 238 (1980)

  55. Virmani, J., Kumar, V., Kalra, N., Khandelwal, N.: Prediction of cirrhosis from liver ultrasound B-mode images based on Laws’ masks analysis. In: 2011 International Conference on Image Information Processing (IEEE, 2011), pp. 1–5

  56. Rachidi, M., Marchadier, A., Gadois, C., Lespessailles, E., Chappard, C., Benhamou, C.L.: Laws’ masks descriptors applied to bone texture analysis: an innovative and discriminant tool in osteoporosis. Skeletal Radiol. 37(6), 541 (2008)

    Google Scholar 

  57. Vince, D., Dixon, K., Cothren, R., Cornhill, J.: Comparison of texture analysis methods for the characterization of coronary plaques in intravascular ultrasound images. Comput. Med. Imaging Graph. 24(4), 221 (2000)

    Google Scholar 

  58. Gan, H., Seng, H., Yan, C., Tan, T.S.: Research on Law’s mask texture analysis system reliability. Res. J. Appl. Sci. Eng. Technol. (2014)

  59. Virmani, J., Thakur, K.S.: Classification of breast tissue density patterns using svm-based hierarchical classifier. In: Classification of Breast Tissue Density Patterns Using SVM-Based Hierarchical Classifier (2019)

  60. Khan, S., Hussain, M., Aboalsamh, H., Mathkour, H., Bebis, G., Zakariah, M.: Optimized Gabor features for mass classification in mammography. Appl. Soft Comput. 44, 267 (2016)

    Google Scholar 

  61. Khan, S., Hussain, M., Aboalsamh, H., Bebis, G.: A comparison of different Gabor feature extraction approaches for mass classification in mammography. Multimed. Tools Appl. 76(1), 33 (2017)

    Google Scholar 

  62. Khan, S., Khan, A., Maqsood, M., Aadil, F., Ghazanfar, M.A.: Optimized gabor feature extraction for mass classification using cuckoo search for big data e-healthcare. J. Grid Comput. 17(2), 239 (2019)

    Google Scholar 

  63. Antonini, M., Barlaud, M., Mathieu, P., Daubechies, I.: Image coding using wavelet transform. IEEE Trans. Image Process. 1(2), 205 (1992)

    Google Scholar 

  64. Lowe, D.G.: Object recognition from local scale-invariant features. In: Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on (1999)

  65. Matsuyama, E., Tsai, D.Y., Lee, Y., Tsurumaki, M., Takahashi, N., Watanabe, H., Chen, H.M.: A modified undecimated discrete wavelet transform based approach to mammographic image denoising. J. Digit. Imaging (2013)

  66. Virmani, J., et al.: Breast tissue density classification using wavelet-based texture descriptors. In: Proceedings of the Second International Conference on Computer and Communication Technologies (Springer, 2016), pp. 539–546

  67. Yaşar, H., Kutbay, U., Hardalaç, F.: A new combined system using ANN and complex wavelet transform for tissue density classification in mammography images. In: 2018 4th International Conference on Computer and Technology Applications (ICCTA) (IEEE, 2018), pp. 179–183

  68. Lindeberg, T.: Scale invariant feature transform (2012)

  69. Bosch, A., Munoz, X., Oliver, A., Marti, J.: Modeling and classifying breast tissue density in mammograms. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06), vol. 2 (IEEE, 2006), vol. 2, pp. 1552–1558

  70. Liasis, G., Pattichis, C., Petroudi, S.: Combination of different texture features for mammographic breast density classification. In: 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE) (IEEE, 2012), pp. 732–737

  71. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971 (2002)

    MATH  Google Scholar 

  72. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: International workshop on analysis and modeling of faces and gestures (Springer, 2007), pp. 168–182

  73. Rampun, A., Scotney, B., Wang, H., Morrow, P.: Local septenary patterns for breast density classification in mammograms. In: Proc. Irish Machine Vision and Image Processing Conference Proceedings 2018, pp. 101–108 (2018)

  74. Hiba, C., Hamid, Z., Omar, A.: An improved breast tissue density classification framework using bag of features model. In: 2016 4th IEEE International Colloquium on Information Science and Technology (CiSt) (IEEE, 2016), pp. 405–409

  75. Rampun, A., Morrow, P., Scotney, B., Winder, J.: Breast density classification using local ternary patterns in mammograms. In: International Conference Image Analysis and Recognition (Springer, 2017), pp. 463–470

  76. Rampun, A., Morrow, P., Scotney, B., Winder, J.: Breast density classification using multiresolution local quinary patterns in mammograms. In: Annual Conference on Medical Image Understanding and Analysis (Springer, 2017), pp. 365–376

  77. Andrik, R., Bryan, S., Philip, M., Wang, H., John, W.: Breast density classification using local quinary patterns with various neighbourhood topologies. J. Imaging 4(1), 14 (2018)

    Google Scholar 

  78. Rampun, A., Scotney, B.W., Morrow, P.J., Wang, H.: Breast density classification using local septenary patterns: a multi-resolution and multi-topology approach. In: 2019 IEEE 32nd International Symposium on Computer-Based Medical Systems (CBMS) (IEEE, 2019), pp. 646–651

  79. Langley, P., et al.: Selection of relevant features in machine learning. In: Proceedings of the AAAI Fall symposium on relevance, vol. 184, vol. 184, pp. 245–271 (1994)

  80. Svante, W., Kim, E., Paul, G.: Chemometrics and intelligent laboratory systems. Principal component analysis (1987)

  81. Abdi, H., Williams, L.J.: Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433 (2010)

    Google Scholar 

  82. Haque, M.S.M., Hassan, M.R., Bin Makhashen, G.M., Owaidh, A., Kamruzzaman, J.: Breast density classification for cancer detection using DCT-PCA feature extraction and classifier ensemble. In: International Conference on Intelligent Systems Design and Applications (Springer, 2017), pp. 702–711

  83. Moayedi, F., Azimifar, Z., Boostani, R., Katebi, S.: Contourlet-based mammography mass classification using the SVM family. Comput. Biol. Med. 40(4), 373 (2010)

    Google Scholar 

  84. Kuo, B.C., Ho, H.H., Li, C.H., Hung, C.C., Taur, J.S.: A kernel-based feature selection method for SVM with RBF kernel for hyperspectral image classification. IEEE J. Select. Top. Appl. Earth Observ. Remote Sens. 7(1), 317 (2013)

    Google Scholar 

  85. Othman, M.F.B., Abdullah, N.B., Kamal, N.F.B.: MRI brain classification using support vector machine. In: 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization (IEEE, 2011), pp. 1–4

  86. Loizou, C.P., Murray, V., Pattichis, M.S., Seimenis, I., Pantziaris, M., Pattichis, C.S.: Multiscale amplitude-modulation frequency-modulation (AM-FM) texture analysis of multiple sclerosis in brain MRI images. IEEE Trans. Inform. Technol. Biomed. 15(1), 119 (2010)

    Google Scholar 

  87. Kriti, J.V.: Comparison of CAD systems for three class breast tissue density classification using mammographic images. In: Comparison of CAD Systems for Three Class Breast Tissue Density Classification Using Mammographic Images (Springer International Publishing, 2016)

  88. Abdullah, N., Ngah, U.K., Aziz, S.A.: Image classification of brain MRI using support vector machine. In: 2011 IEEE International Conference on Imaging Systems and Techniques (IEEE, 2011), pp. 242–247

  89. Selvaraj, H., Selvi, S.T., Selvathi, D., Gewali, L.: Brain MRI slices classification using least squares support vector machine. Int. J. Intell. Comput. Med. Sci. Image Process. 1(1), 21 (2007)

    Google Scholar 

  90. Nithya, R., Santhi, B.: Computer-aided diagnosis system for mammogram density classification. Int. J. Biomed. Eng. Technol (2016)

  91. Cruz-Mota, J., Bogdanova, I., Paquier, B., Bierlaire, M., Thiran, J.P.: Scale invariant feature transform on the sphere: theory and applications. Int. J. Comput. Vis. 98(2), 217 (2012)

    MathSciNet  MATH  Google Scholar 

  92. Kim, J., Kim, B., Savarese, S.: Comparing image classification methods: K-nearest-neighbor and support-vector-machines. In: Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics, vol. 1001 , vol. 1001, pp. 48,109–2122 (2012)

  93. Thanh Noi, P., Kappas, M.: Comparison of random forest, k-nearest neighbor, and support vector machine classifiers for land cover classification using Sentinel-2 imagery. Sensors 18(1), 18 (2018)

    Google Scholar 

  94. Islam, M.J., Wu, Q.J., Ahmadi, M., Sid-Ahmed, M.A.: Investigating the performance of naive-bayes classifiers and k-nearest neighbor classifiers\(\langle \rangle\). In: 2007 International Conference on Convergence Information Technology (ICCIT 2007) (IEEE, 2007), pp. 1541–1546

  95. Azar, A.T., ElSaid, S.A.: Probabilistic neural network for breast cancer classification. Neural Comput. Appl. 23(6), 1737 (2013)

    Google Scholar 

  96. Dontchos, B.N., Yala, A., Barzilay, R., Xiang, J., Lehman, C.D.: External validation of a deep learning model for predicting mammographic breast density in routine clinical practice. Acad. Radiol. (2020)

  97. Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al.: Recent advances in convolutional neural networks. Pattern Recogn. 77, 354 (2018)

    Google Scholar 

  98. Zhang, W., Doi, K., Giger, M.L., Wu, Y., Nishikawa, R.M., Schmidt, R.A.: Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. Med. Phys. 21(4), 517 (1994)

    Google Scholar 

  99. Yoo, Y., Oh, S.Y.: Fast training of convolutional neural network classifiers through extreme learning machines. In: International Joint Conference on Neural Networks (2016)

  100. Al-Saffar, A.A.M., Hai, T., Talab, M.A.: Review of deep convolution neural network in image classification. In: International Conference on Radar (2018)

  101. Guo, T., Dong, J., Li, H., Gao, Y.: Simple convolutional neural network on image classification. In: 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA) (2017)

  102. Li, Q., Cai, W., Wang, X., Yun, Z., Feng, D.D., Mei, C.: Medical image classification with convolutional neural network. In: 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) (2015)

  103. Lecun, Y.: Learning processes in an asymmetric threshold network. In: Disordered systems and biological organization (1986)

  104. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. NIPS (2012)

  105. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions (2014)

  106. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. Comput. Sci. (2014)

  107. He, K, Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 770–778 (2016)

  108. Fonseca, P., Castañeda, B., Valenzuela, R., Wainer, J.: Breast density classification with convolutional neural networks. In: Iberoamerican Congress on Pattern Recognition (2016)

  109. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015)

  110. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826 (2016)

  111. Szegedy, C., Ioffe, S., Vanhoucke, V., A, A.A.: Inception-v4, inception-ResNet and the impact of residual connections on learning (2016)

  112. Kaiser, N., Fieselmann, A., Vesal, S., Ravikumar, N., Maier, A.: Mammographic breast density classification using a deep neural network: assessment on the basis of inter-observer variability. In: Image Perception, Observer Performance, and Technology Assessment (2019)

  113. Wu, N., Geras, K.J., Shen, Y., Su, J., Kim, S.G., Kim, E., Wolfson, S., Moy, L., Cho, K.: Breast density classification with deep convolutional neural networks (2017)

  114. Xu, J., Li, C., Zhou, Y., Mou, L., Zheng, H., Wang, S.: Classifying mammographic breast density by residual learning (2018)

  115. Lee, J., Yoo, D., Huh, J.Y., Kim, H.E.: Photometric transformer networks and label adjustment for breast density prediction. arXiv preprint arXiv:1905.02906 (2019)

  116. Li, C., Xu, J., Liu, Q., Zhou, Y., Mou, L., Pu, Z., Xia, Y., Zheng, H. Wang, S.: Multi-view mammographic density classification by dilated and attention-guided residual learning. IEEE/ACM Trans. Comput. Biol. Bioinform. (2020)

  117. Shi, P., Wu, C., Zhong, J., Wang, H.: Deep learning from small dataset for BI-RADS density classification of mammography images. In: 2019 10th International Conference on Information Technology in Medicine and Education (ITME) (2019)

  118. Deng, J., Ma, Y., Deng-Ao, L., Zhao, J., Zhang, H.: Classification of breast density categories based on SE-attention neural networks. Comput. Methods Prog. Biomed. 193, 105489 (2020)

    Google Scholar 

  119. Moon, W.K., Lo, C.M., Goo, J.M., Bae, M.S., Chang, J.M., Huang, C.S., Chen, J.H., Ivanova, V., Chang, R.F.: Quantitative analysis for breast density estimation in low dose chest CT scans. J. Med. Syst. 38(3), 21 (2014)

    Google Scholar 

  120. Ding, H., Johnson, T., Lin, M., Le, H.Q., Ducote, J.L., Su, M.Y., Molloi, S.: Breast density quantification using magnetic resonance imaging (MRI) with bias field correction: a postmortem study. Med. Phys. 40(12), 122305 (2013)

    Google Scholar 

  121. Gubern-Mérida, A., Kallenberg, M., Platel, B., Mann, R.M., Martí, R., Karssemeijer, N.: Volumetric breast density estimation from full-field digital mammograms: a validation study. PLoS One 9(1), e85952 (2014)

    Google Scholar 

  122. Glide-Hurst, C.K., Duric, N., Littrup, P.: Volumetric breast density evaluation from ultrasound tomography images. Med. Phys. 35(9), 3988 (2008)

    Google Scholar 

  123. Chen, J.H., Huang, C.S., Chien, K.C.C., Takada, E., Moon, W.K., Wu, J.H.K., Cho, N., Wang, Y.F., Chang, R.F.: Breast density analysis for whole breast ultrasound images. Med. Phys. 36(11), 4933 (2009)

    Google Scholar 

  124. Moon, W.K., Chang, J.F., Lo, C.M., Chang, J.M., Lee, S.H., Shin, S.U., Huang, C.S., Chang, R.F.: Quantitative breast density analysis using tomosynthesis and comparison with MRI and digital mammography. Comput. Methods Prog. Biomed. 154, 99 (2018)

    Google Scholar 

  125. Heine, J.J., Carston, M.J., Scott, C.G., Brandt, K.R., Wu, F.F., Pankratz, V.S., Sellers, T.A., Vachon, C.M.: An automated approach for estimation of breast density. Cancer Epidemiol. Biomark. Prev. 17(11), 3090 (2008)

    Google Scholar 

  126. Heine, J.J., Cao, K., Rollison, D.E., Tiffenberg, G., Thomas, J.A.: A quantitative description of the percentage of breast density measurement using full-field digital mammography. Acad. Radiol. 18(5), 556 (2011)

    Google Scholar 

  127. Fowler, E.E.E., Vachon, C.M., Scott, C.G., Sellers, T.A., Heine, J.J.: Automated percentage of breast density measurements for full-field digital mammography applications. Acad. Radiol. 21(8), 958 (2014)

    Google Scholar 

  128. Saha, P.K., Udupa, J.K., Conant, E.F., Chakraborty, D.P., Sullivan, D.: Breast tissue density quantification via digitized mammograms. IEEE Trans. Med. Imaging 20(8), 792 (2001)

    Google Scholar 

  129. Thierry, P.: A new method for grey-level picture thresholding using the entropy of the histogram. Signal Process. (1980)

  130. Tsai, W.H.: Moment-preserving thresholding: a new approach. In: Document Image Analysis (1995)

  131. Mohamed, A., Kaitlyn, T., Gerry, S., Judy, C.: Statistical evaluation of a fully automated mammographic breast density algorithm. Comput. Math. Methods Med. 2013, 1 (2013)

    MathSciNet  MATH  Google Scholar 

  132. Liao, P.S., Chen, T.S., Chung, P.C., et al.: A fast algorithm for multilevel thresholding. J. Inf. Sci. Eng. 17(5), 713 (2001)

    Google Scholar 

  133. Uyun, S., Hartati, S., Harjoko, A., Subanar, L.: Choridah: comparison between automatic and semiautomatic thresholding method for mammographic density classification. Adv. Mater. Res. 896, 672 (2014)

    Google Scholar 

  134. Lee, J., Nishikawa, R.M., Automated mammographic breast density estimation using a fully convolutional network. Med. Phys. (2018)

  135. Shelhamer, E., Long, J., Darrell, T.: Fully Convolutional Networks for Semantic Segmentation\(\langle \rangle\). Fully Convolutional Networks for Semantic Segmentation (IEEE Computer Society, 2017)

  136. Chan, H.P. , Helvie, M.A.: Deep learning for mammographic breast density assessment and beyond (2019)

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Acknowledgements

This work is jointly supported by the Natural Science Foundation of Gansu Province (No. 18JR3RA288) and the Fundamental Research Funds for the Central Universities of China (No. lzujbky-2017-it72 and No. lzujbky-2018-it61).

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  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, China

    Xiaorong Li, Yunliang Qi, Meng Lou, Wenwei Zhao, Jie Meng, Wenjun Zhang & Yide Ma

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Correspondence to Yide Ma.

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Communicated by Ichiro IDE.

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Li, X., Qi, Y., Lou, M. et al. Breast density measurement methods on mammograms: a review. Multimedia Systems 28, 2367–2390 (2022). https://doi.org/10.1007/s00530-022-00955-1

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