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RMID: A Novel and Efficient Image Descriptor for Mammogram Mass Classification

  • Sk Md ObaidullahEmail author
  • Sajib Ahmed
  • Teresa Gonçalves
  • Luís Rato
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 945)

Abstract

For mammogram image analysis, feature extraction is the most crucial step when machine learning techniques are applied. In this paper, we propose RMID (Radon-based Multi-resolution Image Descriptor), a novel image descriptor for mammogram mass classification, which perform efficiently without any clinical information. For the present experimental framework, we found that, in terms of area under the ROC curve (AUC), the proposed RMID outperforms, upto some extent, previous reported experiments using histogram based hand-crafted methods, namely Histogram of Oriented Gradient (HOG) and Histogram of Gradient Divergence (HGD) and also Convolution Neural Network (CNN). We also found that the highest AUC value (0.986) is obtained when using only the carniocaudal (CC) view compared to when using only the mediolateral oblique (MLO) (0.738) or combining both views (0.838). These results thus proves the effectiveness of CC view over MLO for better mammogram mass classification.

Keywords

Image descriptor Mammogram image Breast cancer Classification 

Notes

Acknowledgement

The preliminary version of this paper was presented at the 3rd Conference on Information Technology, Systems Research and Computational Physics, 2–5 July 2018, Cracow, Poland [30]. The authors are thankful for considering the paper in the proceedings.

References

  1. 1.
  2. 2.
    Skaane, P., Hofvind, S., Skjennald, A.: Randomized trial of screen-film versus full-field digital mammography with soft-copy reading in population-based screening program: follow-up and final results of Oslo II study. Radiology 244(3), 708–17 (2007)CrossRefGoogle Scholar
  3. 3.
    Pisano, E.D., Hendrick, R.E., Yaffe, M.J.: for the Digital Mammographic Imaging Screening Trial (DMIST) Investigators Group: Diagnostic accuracy of digital versus film mammography: exploratory analysis of selected population subgroups in DMIST. Radiology 246(2), 376–83 (2008)CrossRefGoogle Scholar
  4. 4.
    Moura, D.C., López, M.A.G.: An evaluation of image descriptors combined with clinical data for breast cancer diagnosis. Int. J. Comput. Assist. Radiol. Surg. 8, 561–574 (2013)CrossRefGoogle Scholar
  5. 5.
    Constantinidis, A.S., Fairhurst, M.C., Rahman, A.F.R.: A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms. Pattern Recognit. 34(8), 1527–1537 (2001)CrossRefGoogle Scholar
  6. 6.
    Belkasim, S.O., Shridhar, M., Ahmadi, M.: Pattern-recognition with moment invariants-a comparative-study and new results. Pattern Recognit. 24(12), 1117–1138 (1991)CrossRefGoogle Scholar
  7. 7.
    Haralick, R.M., Shanmuga, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  8. 8.
    Yu, S.Y., Guan, L.: A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Trans. Med. Imaging 19(2), 115–126 (2000)CrossRefGoogle Scholar
  9. 9.
    Dhawan, A.P., Chitre, Y., Kaiser, B.C., Moskowitz, M.: Analysis of mammographic microcalcifications using gray-level image structure features. IEEE Trans. Med. Imaging 15(3), 246–259 (1996)CrossRefGoogle Scholar
  10. 10.
    Wang, D., Shi, L., Ann, H.P.: Automatic detection of breast cancers in mammograms using structured support vector machines. Neurocomputing 72(13–15), 3296–3302 (2009)CrossRefGoogle Scholar
  11. 11.
    Dua, S., Singh, H., Thompson, H.W.: Associative classification of mammograms using weighted rules. Expert Syst. Appl. 36(5), 9250–9259 (2009)CrossRefGoogle Scholar
  12. 12.
    Sahiner, B., Chan, H.P., Petrick, N., Helvie, M.A., Hadjiiski, L.M.: Improvement of mammographic mass characterization using spiculation measures and morphological features. Med. Phys. 28(7), 1455–1465 (2001)CrossRefGoogle Scholar
  13. 13.
    Claudia, M., Enrique, A., Maria T., Víctor G.C.: Tissues classification of the cardiovascular system using texture descriptors. In: Medical Image Understanding and Analysis, MIUA 2017, pp. 123–132 (2017)Google Scholar
  14. 14.
    Alison, O.N., Matthew, S., Erin, B., Keith, G.: A comparison of texture features versus deep learning for image classification in interstitial lung disease. In: Medical Image Understanding and Analysis, MIUA 2017, pp. 743–753 (2017)Google Scholar
  15. 15.
    Ferreira, C.B.R., Borges, D.B.L.: Analysis of mammogram classification using a wavelet transform decomposition. Pattern Recogn. Lett. 24(7), 973–982 (2003)CrossRefGoogle Scholar
  16. 16.
    Rashed, E.A., Ismail, I.A., Zaki, S.I.: Multiresolution mammogram analysis in multilevel decomposition. Pattern Recogn. Lett. 28(2), 286–292 (2007)CrossRefGoogle Scholar
  17. 17.
    Meselhy, E.M., Faye, I., Belhaouari, S.B.: A comparison of wavelet and curvelet for breast cancer diagnosis in digital mammogram. Comput. Biol. Med. 40(4), 384–391 (2010).  https://doi.org/10.1016/j.compbiomed.2010.02.002CrossRefGoogle Scholar
  18. 18.
    Ramos-Pollán, R., Guevara-López, M., Suárez-Ortega, C., Díaz-Herrero, G., Franco-Valiente, J., Rubio-del-Solar, M., de Posada González, N., Vaz, M., Loureiro, J., Ramos, I.: Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J. Med. Syst. 36(4), 2259–2269 (2011)CrossRefGoogle Scholar
  19. 19.
    Deans, S.R.: Applications of the Radon Transform. Wiley Interscience Publications, New York (1983)zbMATHGoogle Scholar
  20. 20.
    Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)CrossRefGoogle Scholar
  21. 21.
    Huhn, J., Hullermeier, E.: FURIA: an algorithm for unordered fuzzy rule induction. Data Min. Knowl. Discov. 19(3), 293–319 (2009)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefGoogle Scholar
  23. 23.
    Bielza, C., Li, G., Larrañaga, P.: Multi-dimensional classification with Bayesian networks. Int. J. Approx. Reason. 52, 705–727 (2011)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Mika, S., Ratsch, G., Weston, J.: Fisher discriminant analysis with kernels. In: Conference on Neural Networks for Signal Processing IX, pp. 41–48 (1999)Google Scholar
  25. 25.
  26. 26.
    Santosh, K.C., Antani, S.: Automated chest X-ray screening: can lung region symmetry help detect pulmonary abnormalities. IEEE Trans. Med. Imaging (2017).  https://doi.org/10.1109/TMI.2017.2775636CrossRefGoogle Scholar
  27. 27.
    Arevalo, J., González, F.A., Ramos-Pollán, R., Oliveira, J.L., Lopez, M.A.G.: Convolutional neural networks for mammography mass lesion classification. In: International Conference of the Engineering in Medicine and Biology Society (2015)Google Scholar
  28. 28.
    http://bcdr.inegi.up.pt. Accessed 15 Feb 2018
  29. 29.
    Arevalo, J., González, F.A., Ramos-Pollán, R., Oliveira, J.L., Lopez, M.A.G.: Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Programs Biomed. 127, 248–257 (2016)CrossRefGoogle Scholar
  30. 30.
    Obaidullah, Sk. Md., Sajib, A., Teresa, G., Luis, R.: RMID: a novel and efficient image descriptor for mammogram mass classification. In: Kulczycki, P., Kowalski, P.A., Łukasik, S. (eds.) Contemporary Computational Science, p. 203. AGH-UST Press, Cracow (2018)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sk Md Obaidullah
    • 1
    Email author
  • Sajib Ahmed
    • 1
  • Teresa Gonçalves
    • 1
  • Luís Rato
    • 1
  1. 1.Department of InformaticsUniversity of ÉvoraÉvoraPortugal

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