Advertisement

Genetic Programming for the Classification of Levels of Mammographic Density

  • Daniel Fajardo-DelgadoEmail author
  • María Guadalupe Sánchez
  • Raquel Ochoa-Ornelas
  • Ismael Edrein Espinosa-Curiel
  • Vicente Vidal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10841)

Abstract

Breast cancer is the second cause of death of adult women in Mexico. Some of the risk factors for breast cancer that are visible in a mammography are the masses, calcifications, and the levels of mammographic density. While the first two have been studied extensively through the use of digital mammographies, this is not the case for the last one. In this paper, we address the automatic classification problem for the levels of mammographic density based on an evolutionary approach. Our solution comprises the following stages: thresholding, feature extractions, and the implementation of a genetic program. We performed experiments to compare the accuracy of our solution with other conventional classifiers. Experimental results show that our solution is very competitive and even outperforms the other classifiers in some cases.

Keywords

Breast cancer Levels of mammographic density Genetic programming 

Notes

Acknowledgments

This work was supported by PRODEP under grant 511-6/17-8931 (ITCGUZ-CA-7), and by the TecNM under the projects 6055.17-P and 6307.17-P. Additionally, it was partially funded by the Spanish Ministry of Economy and Competitiveness under grant TIN2015-66972-C5-4-R co-financed by FEDER funds.

References

  1. 1.
    Knaul, F.M., Nigenda, G., Lozano, R., Arreola-Ornelas, H., Langer, A., Frenk, J.: Breast cancer in Mexico: a pressing priority. Reprod. Health Matter 16(32), 113–123 (2008)CrossRefGoogle Scholar
  2. 2.
    Lozano, R., Knaul, F., Gómez-Dantés, H., Arreola-Ornelas, H., Méndez, O.: Trends in mortality of breast cancer in Mexico, 1979–2006, observatory of health. Work Document. Competitiveness and Health, Mexican Foundation for the Health (2008). (in Spanish)Google Scholar
  3. 3.
    Franco-Marina, F., Lazcano-Ponce, E., López-Carrillo, L.: Breast cancer mortality in Mexico: an age-period-cohort analysis. Pub. Health Mex. 51, s157–s164 (2009). (in Spanish)Google Scholar
  4. 4.
    Tyrer, J., Duffy, S.W., Cuzick, J.: A breast cancer prediction model incorporating familial and personal risk factors. Stat. Med. 23(7), 1111–1130 (2004)CrossRefGoogle Scholar
  5. 5.
    Brandan, M.E., Villaseñor, Y.: Detection of breast cancer: state of the mammography in Mexico. Cancerology 1(3), 14–162 (2006). (in Spanish)Google Scholar
  6. 6.
    Byrne, C.: Studying mammographic density: implications for understanding breast cancer. JNCI-J. Natl. Cancer Inst. 89(8), 531–532 (1997)CrossRefGoogle Scholar
  7. 7.
    Wolfe, J.: Breast patterns as an index of risk for developing breast cancer. Am. J. Roentgenol. 126(6), 1130–1137 (1976)CrossRefGoogle Scholar
  8. 8.
    Obenauer, S., Hermann, K., Grabbe, E.: Applications and literature review of the BI-RADS classification. Eur. Radiol. 15(5), 1027–1036 (2005)CrossRefGoogle Scholar
  9. 9.
    Burling-Claridge, F., Iqbal, M., Zhang, M.: Evolutionary algorithms for classification of mammographie densities using local binary patterns and statistical features. In: IEEE Congress on Evolutionary Computation (CEC), pp. 3847–3854 (2016)Google Scholar
  10. 10.
    Qian, W., Li, L., Clarke, L.P.: Image feature extraction for mass detection in digital mammography: influence of wavelet analysis. Med. Phys. 26(3), 402–408 (1999)CrossRefGoogle Scholar
  11. 11.
    Chan, T.F., Golub, G.H., LeVeque, R.J.: Updating formulae and a pairwise algorithm for computing sample variances. In: Caussinus, H., Ettinger, P., Tomassone, R. (eds.) COMPSTAT 1982 5th Symposium held at Toulouse 1982, pp. 30–41. Springer, Heidelberg (1982).  https://doi.org/10.1007/978-3-642-51461-6_3CrossRefGoogle Scholar
  12. 12.
    Polakowski, W.E., Cournoyer, D.A., Rogers, S.K., DeSimio, M.P., Ruck, D.W., Hoffmeister, J.W., Raines, R.A.: Computer-aided breast cancer detection and diagnosis of masses using difference of Gaussians and derivative-based feature saliency. IEEE Trans. Med. Imaging 16(6), 811–819 (1997)CrossRefGoogle Scholar
  13. 13.
    Li, L., Clark, R.A., Thomas, J.A.: Computer-aided diagnosis of masses with full-field digital mammography. Acad. Radiol. 9(1), 4–12 (2002)CrossRefGoogle Scholar
  14. 14.
    Reyad, Y.A., Berbar, M.A., Hussain, M.: Comparison of statistical, LBP, and multi-resolution analysis features for breast mass classification. J. Med. Syst. 38(9), 100 (2014)CrossRefGoogle Scholar
  15. 15.
    Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)CrossRefGoogle Scholar
  16. 16.
    Ding, S., Zhu, H., Jia, W., Su, C.: A survey on feature extraction for pattern recognition. Artif. Intell. Rev. 37(3), 169–180 (2012)CrossRefGoogle Scholar
  17. 17.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  18. 18.
    Gonzalez, R.C., Woods, R.E.: Image processing. Digital Image Process. 2 (2007)Google Scholar
  19. 19.
    Daubechies, I., et al.: Ten Lectures on Wavelets. CBMS-NSF Regional Conference Series in Applied Mathematics, vol. 61. SIAM, Philadelphia (1991)zbMATHGoogle Scholar
  20. 20.
    Espejo, P.G., Ventura, S., Herrera, F.: A survey on the application of genetic programming to classification. IEEE Trans. Syst. Man Cybern. 40(2), 121–144 (2010)CrossRefGoogle Scholar
  21. 21.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  22. 22.
    Moreira, I.C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M.J., Cardoso, J.S.: Inbreast: toward a full-field digital mammographic database. Acad. Radiol. 19(2), 236–248 (2012)CrossRefGoogle Scholar
  23. 23.
    Suckling, J., Parker, J., Dance, D., Astley, S., Hutt, I., Boggis, C., Ricketts, I., Stamatakis, E., Cerneaz, N., Kok, S., et al.: The mammographic image analysis society digital mammogram database. Exerpta Medica. Int. Congr. Ser. 1069, 375–378 (1994)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Daniel Fajardo-Delgado
    • 1
    Email author
  • María Guadalupe Sánchez
    • 1
  • Raquel Ochoa-Ornelas
    • 1
  • Ismael Edrein Espinosa-Curiel
    • 2
  • Vicente Vidal
    • 3
  1. 1.Department of Systems and ComputationITCGCiudad GuzmánMexico
  2. 2.Department of Computer ScienceCICESE-UT3TepicMexico
  3. 3.Department of Informatics Systems and ComputingUniversitat Politècnica de ValènciaValenciaSpain

Personalised recommendations