Breast Density Classification for Cancer Detection Using DCT-PCA Feature Extraction and Classifier Ensemble

  • Md Sarwar Morshedul Haque
  • Md Rafiul Hassan
  • G. M. BinMakhashen
  • A. H. Owaidh
  • Joarder Kamruzzaman
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)


It is well known that breast density in mammograms may hinder the accuracy of diagnosis of breast cancer. Although the dense breasts should be processed in a special manner, most of the research has treated dense breast almost the same as fatty. Consequently, the dense tissues in the breast are diagnosed as a developed cancer. In contrast, dense-fatty should be clearly distinguished before the diagnosis of cancerous or not cancerous breast. In this paper, we develop such a system that will automatically analyze mammograms and identify significant features. For feature extraction, we develop a novel system by combining a two-dimensional discrete cosine transform (2D-DCT) and a principal component analysis (PCA) to extract a minimal feature set of mammograms to differentiate breast density. These features are fed to three classifiers: Backpropagation Multilayer Perceptron (MLP), Support Vector Machine (SVM) and K Nearest Neighbour (KNN). A majority voting on the outputs of different machine learning tools is also investigated to enhance the classification performance. The results show that features extracted using a combination of DCT-PCA provide a very high classification performance while using a majority voting of classifiers outputs from MLP, SVM, and KNN.


Breast cancer Breast Dense and Fatty DCT PCA Machine learning tools Pattern Recognition 


  1. 1.
    NIH: comprehensive cancer information - national cancer institute. Accessed 12 June 2017
  2. 2.
    Silva, W., Menotti, D.: Classification of mammograms by the breast composition. In: Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), WorldComp, pp. 1–6 (2012)Google Scholar
  3. 3.
    Farag, A., Mashali, S.: DCT based features for the detection of microcalcificationsin digital mammograms. In: 2003 IEEE 46th Midwest Symposium on Circuits and Systems, vol. 1., pp. 352–355. IEEE (2003)Google Scholar
  4. 4.
    Komen, S.G.: Understanding breast cancer. Accessed 12 June 2017
  5. 5.
    Chen, W., Er, M.J., Wu, S.: PCA and LDA in DCT domain. Pattern Recogn. Lett. 26(15), 2474–2482 (2005)CrossRefGoogle Scholar
  6. 6.
    Prathibha, B., Sadasivam, V.: An analysis on breast tissue characterization in combined transform domain using nearest neighbor classifiers. In: 2011 International Conference on Computer, Communication and Electrical Technology (ICCCET), pp. 50–54. IEEE (2011)Google Scholar
  7. 7.
    Mudigonda, N.R., Rangayyan, R., Desautels, J.L.: Gradient and texture analysis for the classification of mammographic masses. IEEE Trans. Med. Imaging 19(10), 1032–1043 (2000)CrossRefGoogle Scholar
  8. 8.
    Tan, M., Zheng, B., Leader, J.K., Gur, D.: Association between changes in mammographic image features and risk for near-term breast cancer development. IEEE Trans. Med. Imaging 35(7), 1719–1728 (2016)CrossRefGoogle Scholar
  9. 9.
    Hussain, M.: False positive reduction using Gabor feature subset selection. In: 2013 International Conference on Information Science and Applications (ICISA), pp. 1–5. IEEE (2013)Google Scholar
  10. 10.
    Muthukarthigadevi, R., Anand, S.: Detection of architectural distortion in mammogram image using wavelet transform. In: 2013 International Conference on Information Communication and Embedded Systems (ICICES), pp. 638–643. IEEE (2013)Google Scholar
  11. 11.
    Oliver, A., Freixenet, J., Marti, R., Pont, J., Pérez, E., Denton, E.R., Zwiggelaar, R.: A novel breast tissue density classification methodology. IEEE Trans. Inf. Technol. Biomed. 12(1), 55–65 (2008)CrossRefGoogle Scholar
  12. 12.
    Ganesan, K., Acharya, U.R., Chua, C.K., Min, L.C., Abraham, K.T., Ng, K.H.: Computer-aided breast cancer detection using mammograms: a review. IEEE Rev. Biomed. Eng. 6, 77–98 (2013)CrossRefGoogle Scholar
  13. 13.
    Basheer, N.M., Mohammed, M.H.: Segmentation of breast masses in digital mammograms using adaptive median filtering and texture analysis. Int. J. Recent Technol. Eng. (IJRTE) 2(1), 39–43 (2013)MathSciNetGoogle Scholar
  14. 14.
    Liu, H., Guo, Q., Xu, M., Shen, I.F.: Fast image segmentation using region merging with a k-nearest neighbor graph. In: 2008 IEEE Conference on Cybernetics and Intelligent Systems, pp. 179–184. IEEE (2008)Google Scholar
  15. 15.
    El-Alfy, E.S.M., BinMakhashen, G.M.: Improved personal identification using face and hand geometry fusion and support vector machines. Networked Digit. Technol. 294, 253–261 (2012)CrossRefGoogle Scholar
  16. 16.
    Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Scholkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods-support Vector Learning (1998)Google Scholar
  17. 17.
    Kuncheva, L.I., Whitaker, C.J., Shipp, C.A., Duin, R.P.: Limits on the majority vote accuracy in classifier fusion. Pattern Anal. Appl. 6(1), 22–31 (2003)MathSciNetCrossRefzbMATHGoogle Scholar
  18. 18.
    Suckling, J.: Mammographic image analysis society (2017). Accessed 12 June 2017
  19. 19.
    Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, vol. 14, pp. 1137–1145, Stanford, CA (1995)Google Scholar
  20. 20.
    Charalambous, C.: Conjugate gradient algorithm for efficient training of artificial neural networks. IEE Proc. G (Circ. Devices Syst.) 139(3), 301–310 (1992)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Md Sarwar Morshedul Haque
    • 1
  • Md Rafiul Hassan
    • 1
  • G. M. BinMakhashen
    • 1
  • A. H. Owaidh
    • 1
  • Joarder Kamruzzaman
    • 2
    • 3
  1. 1.King Fahd University of Petroleum and MineralsDhahranKingdom of Saudi Arabia
  2. 2.Federation University AustraliaBallaratAustralia
  3. 3.Monash UniversityMelbourneAustralia

Personalised recommendations