Advertisement

Breast Cancer Classification Using Fuzzy Central Moments

  • H. D. Cheng
  • Y. G. Hu
  • D. L. Hung
  • C. Y. Wu
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 83)

Abstract

Breast cancer continues to be one of the most deadly diseases among American women, which is the second leading cause of cancer-related mortality among American women. Currently there are more than 50 million women over the age of 40 at risk of breast cancer and approximately 144,000 new cases of breast cancer are expected each year in the United States. One out of eight women will develop breast cancer at some point during her lifetime in this country [1,2]. Because of the high incidence of breast cancer, any improvement in the process of diagnosing the disease may have a significant impact on saving lives and cutting costs in the health care system. Since the cause of breast cancer remains unknown and the earlier stage tumors can be more easily and less expensively treated, early detection is the key to breast cancer control. Mammography has proven to be the most reliable method and the major diagnosis means for detecting and classifying breast cancer in the early stage. Studies have shown a decrease in both severe breast cancer and mortality in women who undergo regular mammographic screens [3].

Keywords

Breast Cancer Gray Level Markov Random Field Fuzzy Entropy Fuzzy Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    C. C. Boring, T. S. Squires, T. Tong, and S. Montgomery, “Cancer statistics”, CA-A Cancer J. Clinicians, Vol. 44, pp. 7–26, 1994.CrossRefGoogle Scholar
  2. 2.
    E. Marshall, “Search for a kill: Focus shits from fat to hormones” , Sci., Vol. 259, pp. 618–621, 1995.CrossRefGoogle Scholar
  3. 3.
    I. Andersson and B. F. Sigfusson, “Screening for breast cancer in Malmo: A randomized trial” , Recent Results in Cancer Research, Vol. 105, pp. 62–66, 1987.PubMedCrossRefGoogle Scholar
  4. 4.
    S-M Lai, X. Li, and W. F. Bischof, “On techniques for detecting circumscribed masses in mammograms” , IEEE Trans. Med. Imag., Vol. 8, No. 4, pp. 337–386, 1989.CrossRefGoogle Scholar
  5. 5.
    F. F. Yin, M. L. Giger, K. Doi, C. E. Metz, C. J. Vyborny and R. A. Schmidt, “Computerized detection of masses in digital mammograms: Analysis of bilateral subtraction images” , Medical Physics, Vol. 18, No. 5, pp. 955–963, Oct. 1991.PubMedCrossRefGoogle Scholar
  6. 6.
    Y. Wu, M. L. Giger, K. Doi, C. J. Vyborny, R. A. Schmidt, and C. E. Metz, “Artificial neural networks in mammography: Application to decision making in the diagnosis of breast cancer” , Radiology, Vol. 187, No. 1, pp. 81–87, April 1993.PubMedGoogle Scholar
  7. 7.
    H. D. Li, M. Kallergi, L. P. Clarke, V. K. Jain and R. A. Clark, “Markov random field for tumor detection in digital mammography”, IEEE Trans. Med. Imag., Vol. 14, No. 3, pp. 565–576, 1995.CrossRefGoogle Scholar
  8. 8.
    H. Kobatake and Y. Yoshinaga, “Detection of spicules on mammogram based on skeleton analysis” , IEEE Trans. Med. Imag., Vol. 15, No. 3, pp. 235–245, June 1996.CrossRefGoogle Scholar
  9. 9.
    R. Gordon and R. M. Rangayyan, “Feature enhancement of film mammograms using fixed and adaptive neighborhoods” , Applied Optics, Vol. 23, No. 4, pp. 560–564, 1984.PubMedCrossRefGoogle Scholar
  10. 10.
    A. P. Dhawan and E. L. Royer, “Mammographic feature enhancement by computerized image processing” , Computer Methods and Programs in Biomedicine, Vol. 27, pp. 23–35, 1988.PubMedCrossRefGoogle Scholar
  11. 11.
    W. M. Morrow, R. B. Paranjape, R. M. Rangayyan, and J. E. L. Desautels, “Region-based contrast enhancement of mammograms” , IEEE Trans. Med. Imag., Vol. 11, No. 3, pp. 392–406, 1992.CrossRefGoogle Scholar
  12. 12.
    N. Petrick, Heanf-Ping Chan, B. Sahiner and D. Wei, “An adaptive densityweighted contrast enhancement filter for mammographic breast mass detection”, IEEE Trans. Med. Imag., Vol. 15, No. 1, pp. 59–67, Feb. 1996.CrossRefGoogle Scholar
  13. 13.
    L. A. Zadeh, “Probability measures of fuzzy events” , Journal of Mathematical Analysis and Applications, Vol. 23, pp. 421–427, 1968.CrossRefGoogle Scholar
  14. 14.
    James C. Bezdek, “Fuzzy models — what are they, and why?” , IEEE Trans. on Fuzzy Systems, Vol. 1, No. 1, February 1993.Google Scholar
  15. 15.
    X. Li, Z. Zhao and H. D. Cheng, “Fuzzy entropy threshold approach to breast cancer detection”, Information Sciences, An International Journal, Applications, Vol. 4, No. 1, 1995.Google Scholar
  16. 16.
    L. Chen, H. D. Cheng and J. Zhang, “Fuzzy subfiber and its application to seismic lithology classification” , Information Sciences, Applications, An International Journal, Vol. 1, No. 2, March 1994.Google Scholar
  17. 17.
    H. D. Cheng, J. R. Chen and J. Li, “Threshold selection based on fuzzy cpartition entropy approach”, Pattern Recognition, Vol. 31, No. 7, pp. 857–870, 1998.CrossRefGoogle Scholar
  18. 18.
    H. D. Cheng, Y. M. Lui, and R. I. Freimanis, “A novel approach to microcalcification detection using fuzzy logic technique”, IEEE Trans. Med. Imag., Vol. 17, No. 3, pp. 442–450, June 1998.CrossRefGoogle Scholar
  19. 19.
    H. D. Cheng and H. J. Xu, “A novel fuzzy logic approach to contrast enhancement” , Pattern Recognition, Vol. 33, No. 5, pp. 809–819, May 2000.CrossRefGoogle Scholar
  20. 20.
    M. T. Hagan, H. B. Demuth and M. Beale, Neural Network Design, PSW Publishing, 1996.Google Scholar
  21. 21.
    S. K. Pal and D. K. D. Majumder, Fuzzy Mathematical Approach to Pattern Recognition, John Wiley & Sons, 1986.Google Scholar
  22. 22.
    S. K. Pal and R. A. King, “Image enhancement using smoothing with fuzzy sets” , IEEE Trans. on System, Man and Cybernetics, Vol. 11, No. 7, pp. 404–501, July 1981.Google Scholar
  23. 23.
    N. R. Pal and S. K. Pal, “Entropy: A new definition and its applications”, IEEE Trans. Syst., Man Cybernetics, vol. 21, no. 5, pp. 1260–1270, 1991.CrossRefGoogle Scholar
  24. 24.
    R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd Edition. Addison-Wesley, MA, 1992.Google Scholar
  25. 25.
    M. K. Hu, “Visual pattern recognition by moment invariants” , IRE Trans. on Information Theory, IT-8, pp. 179–187, Feb. 1962.Google Scholar
  26. 26.
    C. H. Teh and R. T. Chin, “On image analysis by the methods of moments”, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 10, No. 4, pp. 496–512, July 1988.CrossRefGoogle Scholar
  27. 27.
    M. R. Teague, “Image analysis via the general theory of moments” , J. Opt. Soc. Am., Vol. 70, No. 8, pp. 920–930, Aug. 1980.CrossRefGoogle Scholar
  28. 28.
    S. Haykin, Neural Networks — A Comprehensive Foundation, Macmillan College Publishing Company, Inc., 1994.Google Scholar
  29. 29.
    E. Gose, R. Johnsonbaugh, and S. Jost, Pattern Recognition and Image Analysis, Prentice Hall, New Jersey, 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • H. D. Cheng
    • 1
  • Y. G. Hu
    • 1
  • D. L. Hung
    • 2
  • C. Y. Wu
    • 1
  1. 1.Department of Computer ScienceUtah State UniversityLoganUSA
  2. 2.Department of Computer, Information and Systems EngineeringSan Jose State UniversitySan JoseUSA

Personalised recommendations