Breast density analysis based on glandular tissue segmentation and mixed feature extraction

  • Xiaonan Gong
  • Zhen Yang
  • Deyuan Wang
  • Yunliang Qi
  • Yanan Guo
  • Yide MaEmail author


Breast cancer poses a threat to the lives of many women. Breast density is a closely related indicator of breast cancer risk. The aim of this paper is to propose a classification system for breast density, which can appropriately segment the glandular tissue from the whole breast and to achieve a better classification result. A new threshold method is applied to segment the breast glandular tissue. The gray level co-occurrence matrix (GLCM) is implemented to extract the texture features of the glandular tissue. Meanwhile, we obtain three statistical features (mean, skewness, kurtosis). In addition, the calculated breast density that is served as a new feature is added to the feature vectors. The mixed feature vectors are classified by Support Vector Machine (SVM) and Ultimate Learning Machine (ELM). Ten-fold cross-validation is used to verify the classifier performance. The system using the SVM achieves 96.19% accuracy for three density types in the MIAS database and achieves 96.35% accuracy of four density types in the DDSM database. The accuracy in the database mixed with the local database was 95.01% and there are three density types in the mixed database. The experimental results indicate that the system proposed has a better performance in breast density classification. The system proposed in this paper can be considered to help the physician to classify breast density.


Breast cancer Breast density Threshold segmentation Feature extraction SVM 



Digital database for screening mammography


Mammographic image analysis society


Computer-aided diagnosis


Region of interest


Segmented whole breast


Segmented glandular tissue


Breast imaging-reporting and data system


Gray level co-occurrence matrix



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).


  1. 1.
    Anguita D, Ridella S, Rivieccio F (2005) K-fold generalization capability assessment for support vector classifiers. in Proceedings. 2005 IEEE International Joint Conference on Neural Networks. 2005, IEEEGoogle Scholar
  2. 2.
    Anguita D et al (2009) K-Fold Cross Validation for Error Rate Estimate in Support Vector Machines. in DMINGoogle Scholar
  3. 3.
    Anthony G, Gregg H, Tshilidzi M (2007) Image classification using SVMs: one-against-one vs one-against-all. arXiv preprint arXiv:0711.2914Google Scholar
  4. 4.
    Arnau OIM, Jordi FIB, Zwiggelaar R (2005) Automatic classification of breast density. Lect Notes Comput Sci 3523:431–438Google Scholar
  5. 5.
    Arnau O et al (2015) Breast Density Analysis Using an Automatic Density Segmentation Algorithm. J Digit Imaging 28(5):604–612Google Scholar
  6. 6.
    Blot L, Zwiggelaar R (2001) Background texture extraction for the classification of mammographic parenchymal patterns. Miua:145–148Google Scholar
  7. 7.
    Bosch A, et al (2006) Modeling and Classifying Breast Tissue Density in Mammograms. In IEEE Computer Society Conference on Computer Vision and Pattern RecognitionGoogle Scholar
  8. 8.
    Bouyahia S, Mbainaibeye J, Ellouze N (2004) Computer-aided diagnosis of mammographic images. First International Symposium on Control, Communications and Signal Processing 2004Google Scholar
  9. 9.
    Bovis K, Singh S (2002) Classification of Mammographic Breast Density Using a Combined Classifier Paradigm. International Workshop on Digital Mammography:177–180Google Scholar
  10. 10.
    Breast Cancer U.K. (2017) Key facts about breast cancer.
  11. 11.
    Breast Cancer: U.S. Breast Cancer Statistics (2017). Accessed 10 Mar 2017.
  12. 12.
  13. 13.
    Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. 2(3): p. 1–27.Google Scholar
  14. 14.
    Chen Z, Denton E, Zwiggelaar R (2011) Local Feature Based Mammographic Tissue Pattern Modelling and Breast Density Classification. In: 2011 4th international conference on biomedical engineering and informaticsGoogle Scholar
  15. 15.
    Chen, D., et al. (2013) The Correlation Analysis between Breast Density and Cancer Risk Factor in Breast MRI Images. In: 2013 International Symposium on Biometrics and Security TechnologiesGoogle Scholar
  16. 16.
    Chen W et al (2016) Cancer statistics in China, 2015. CA Cancer J Clin 66(2):115Google Scholar
  17. 17.
    Clausi DA (2002) An analysis of co-occurrence texture statistics as a function of grey level quantization. Can J Remote Sens 28(1):45–62Google Scholar
  18. 18.
    Cortes C, Vapnik V (1995) Support-Vector Networks. Mach Learn 20(3):273–297zbMATHGoogle Scholar
  19. 19.
    Elmoufidi A et al (2015) Automatically density based breast segmentation for mammograms by using dynamic K-means algorithm and Seed Based Region Growing. Instrumentation and Measurement Technology ConferenceGoogle Scholar
  20. 20.
    Elshinawy M et al (2011) Effect of breast density in selecting features for normal mammogram detection. In: IEEE International Symposium on Biomedical Imaging: From Nano To MacroGoogle Scholar
  21. 21.
    Engeland Sv et al (2006) Volumetric breast density estimation from full-field digital mammograms. IEEE Trans Med Imaging 25(3):273–282Google Scholar
  22. 22.
    Giuliano V, Giuliano C (2013) Volumetric Breast Ultrasound as a Screening Modality in Mammographically Dense Breasts. ISRN Radiology 2013:235270–235270Google Scholar
  23. 23.
    Gonzalez RC, Woods RE (2007) Digital Image Processing (3rd Edition). Prentice-Hall, Inc., Upper Saddle river, pp 1160–1165Google Scholar
  24. 24.
    Gualtieri JA, Cromp RF (1999) Support vector machines for hyperspectral remote sensing classification. in 27th AIPR Workshop: Advances in Computer-Assisted Recognition. International Society for Optics and Photonics.Google Scholar
  25. 25.
    Gubern-Mérida A et al (2015) Breast Segmentation and Density Estimation in Breast MRI: A Fully Automatic Framework. IEEE Journal of Biomedical and Health Informatics 19(1):349–357Google Scholar
  26. 26.
    Guo YN et al (2016) A new method of detecting micro-calcification clusters in mammograms using contourlet transform and non-linking simplified PCNN. Comput Methods Prog Biomed 130(C):31–45Google Scholar
  27. 27.
    Haralick RM, Shanmugam K, Dinstein IH, Haralick RM, Shanmuga K (1973) Dinstein ITextural features for image classification. IEEE Trans Syst Man Cybern 3(6):610–621Google Scholar
  28. 28.
    He W et al (2011) Mammographic image segmentation and risk classification based on mammographic parenchymal patterns and geometric moments. Biomedical Signal Processing and Control 6(3):321–329Google Scholar
  29. 29.
    Heath M et al (2001) The Digital Database for Screening MammographyGoogle Scholar
  30. 30.
    Hsu CW (2002) and C.J. Lin, A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(4):1026Google Scholar
  31. 31.
    Huang CL, Wang CJ (2006) A GA-based feature selection and parameters optimizationfor support vector machines. Expert Syst Appl 31(2):231–240Google Scholar
  32. 32.
    Huang GB, Zhu QY, Siew CK (2006) Extreme learning machine: Theory and applications. Neurocomputing 70(1–3):489–501Google Scholar
  33. 33.
    Kohavi RA (1995) Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection. In: International Joint Conference on Artificial IntelligenceGoogle Scholar
  34. 34.
    Kumar I, Bhadauria HS, Virmani J (2015) Wavelet Packet Texture Descriptors Based Four-class BIRADS Breast Tissue Density Classification. Procedia Computer Science 70:76–84Google Scholar
  35. 35.
    Kumar I, Virmani J, Bhadauria HS (2015) A review of breast density classification methods. In: International Conference on Computing for Sustainable Global DevelopmentGoogle Scholar
  36. 36.
    Kumar I et al (2017) A classification framework for prediction of breast density using an ensemble of neural network classifiers. Biocybernetics & Biomedical Engineering 37(1):217–228Google Scholar
  37. 37.
    Liberman L, Menell JH (2002) Breast imaging reporting and data system (BI-RADS). Radiol Clin N Am 40(3):409–430Google Scholar
  38. 38.
    Lin SW et al (2008) Parameter determination of support vector machine and feature selection using simulated annealing approach. Appl Soft Comput 8(4):1505–1512Google Scholar
  39. 39.
    Liu L, Wang J, He K (2010) Breast density classification using histogram moments of multiple resolution mammograms. International Conference on Biomedical Engineering and InformaticsGoogle Scholar
  40. 40.
    Machida Y et al (2015) Breast density: the trend in breast cancer screening. Breast Cancer 22(3):253–261Google Scholar
  41. 41.
    Malkov S et al (2016) Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status. Breast Cancer Research : BCR 18(1):122Google Scholar
  42. 42.
    Manduca A et al (2009) Texture features from mammographic images and risk of breast cancer. Cancer epidemiology, biomarkers & prevention: a publication of the American Association for Cancer Research. American Society of Preventive Oncology 18(3):837–845Google Scholar
  43. 43.
    Marchette DJ, Lorey RA, Priebe CE (1997) An analysis of local feature extraction in digital mammography. Pattern Recogn 30(9):1547–1554Google Scholar
  44. 44.
    Martin JE, Moskowitz M (1979) and Milbrath, Breast cancer missed by mammography. Am J Roentgenol 132(5):737–739Google Scholar
  45. 45.
    Muhimmah I (2006) Mammographic Density Classification Using Multi Resolution Histogram InformationGoogle Scholar
  46. 46.
    Mustra M, Grgic M (2013) Dense tissue segmentation in digitized mammograms. in Elmar, 2013 International SymposiumGoogle Scholar
  47. 47.
    Mustra M, Grgic M, Delac K (2010) Feature selection for automatic breast density classification. in ELMAR, 2010 ProceedingsGoogle Scholar
  48. 48.
    Nagata C et al (2005) Mammographic density and the risk of breast cancer in Japanese women. Br J Cancer 92:2102Google Scholar
  49. 49.
    Oliveira JEED, Araújo ADA, Deserno TM (2011) Content-based image retrieval applied to BI-RADS tissue classification in screening mammography. World Journal of Radiology 3(1):24Google Scholar
  50. 50.
    Oliver A et al (2008) A novel breast tissue density classification methodology. IEEE Transactions on Information Technology in Biomedicine A Publication of the IEEE Engineering in Medicine & Biology Society 12(1):55–65Google Scholar
  51. 51.
    Otsu N (1979) A Threshold Selection Method From Grey Level Histograms. IEEE Transactions on Systems, Man, and Cybernetics 9:62–66Google Scholar
  52. 52.
    Petroudi S, Kadir T, Brady M (2003) Automatic classification of mammographic parenchymal patterns: a statistical approach. In Engineering in Medicine and Biology Society, 2003. Proceedings of the International Conference of the IEEEGoogle Scholar
  53. 53.
    Petroudi S et al (2013) Investigation of AM-FM methods for mammographic breast density classification. IEEE International Conference on Bioinformatics and BioengineeringGoogle Scholar
  54. 54.
    Pham DL, Xu C, Prince JL (2000) Current Methods in Medical Image Segmentation. Annu Rev Biomed Eng 2(1):315–337Google Scholar
  55. 55.
    Rampun A, et al (2017) Breast Density Classification Using Multiresolution Local Quinary Patterns in MammogramsGoogle Scholar
  56. 56.
    Remes V Haindl M (2015) Classification of breast density in X-ray mammography. International Workshop on Computational Intelligence for Multimedia UnderstandingGoogle Scholar
  57. 57.
    Schölkopf B, Smola A (2001) Learning with kernels : support vector machines, regularization, optimization, and beyond. MIT Press, Cambridge, pp 781–781Google Scholar
  58. 58.
    Siegel RL, Miller KD, Jemal A (2018) Cancer statistics, 2018. CA Cancer J Clin 68(1):7–30Google Scholar
  59. 59.
    Sivaramakrishna R et al (2001) Automatic segmentation of mammographic density. Acad Radiol 8(3):250Google Scholar
  60. 60.
    Soh L-K, Tsatsoulis C (1999) Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans Geosci Remote Sens 37(2):780–795Google Scholar
  61. 61.
    Sohn G et al (2014) Reliability of the percent density in digital mammography with a semi-automated thresholding method. J Breast Cancer 17(2):174–179Google Scholar
  62. 62.
    Strand F et al (2016) Novel mammographic image features differentiate between interval and screen-detected breast cancer: a case-case study. Breast Cancer Res 18(1):100Google Scholar
  63. 63.
    Subashini TS, Ramalingam V, Palanivel S (2010) Automated assessment of breast tissue density in digital mammograms. Computer Vision & Image Understanding 114(1):33–43Google Scholar
  64. 64.
    Suckling J, Parker J, Dance DR (1994) Themammographic image analysis society digital mammogram database. in Int Work on Dig MammographyGoogle Scholar
  65. 65.
    Tzikopoulos SD et al (2011) A fully automated scheme for mammographic segmentation and classification based on breast density and asymmetry. Comput Methods Prog Biomed 102(1):47–63Google Scholar
  66. 66.
    Van Engeland S et al (2006) Volumetric breast density estimation from full-field digital mammograms. IEEE Trans Med Imaging 25(3):273Google Scholar
  67. 67.
    Wang J et al (2017) Automatic Estimation of Volumetric Breast Density Using Artificial Neural Network-Based Calibration of Full-Field Digital Mammography: Feasibility on Japanese Women With and Without Breast Cancer. J Digit Imaging 30(2):215–227Google Scholar
  68. 68.
    Wei C, Gwo C, Li Y (2015) A Framework of Breast Density Estimation System for Breast Magnetic Resonance Images. In 2015 2nd International Conference on Information Science and Control EngineeringGoogle Scholar
  69. 69.
    Wei CH, Gwo CY, Li Y (2015) A Framework of Breast Density Estimation System for Breast Magnetic Resonance Images. International Conference on Information Science and Control EngineeringGoogle Scholar
  70. 70.
    Wolfe JN (1976) Risk for breast cancer development determined by mammographic parenchymal pattern. Cancer 37(5):2486Google Scholar
  71. 71.
    Yaffe MJ (2008) Mammographic density. Measurement of mammographic density. Breast Cancer Research Bcr 10(3):209Google Scholar
  72. 72.
    Zhengyou L, Xiaoshan G (2015) A segmentation method for mammogram x-ray image based on image enhancement with wavelet fusionGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Xiaonan Gong
    • 1
  • Zhen Yang
    • 1
  • Deyuan Wang
    • 1
  • Yunliang Qi
    • 1
  • Yanan Guo
    • 1
  • Yide Ma
    • 1
    Email author
  1. 1.School of Information Science EngineeringLanzhou UniversityLanzhouChina

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