Skip to main content
SpringerLink
Log in

Breast Cancer Classification Using Fuzzy Central Moments

  • Chapter
Fuzzy Logic in Medicine

Part of the book series: Studies in Fuzziness and Soft Computing ((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].

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  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.

    Article  CAS  Google Scholar 

  2. E. Marshall, “Search for a kill: Focus shits from fat to hormones” , Sci., Vol. 259, pp. 618–621, 1995.

    Article  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    PubMed  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  13. L. A. Zadeh, “Probability measures of fuzzy events” , Journal of Mathematical Analysis and Applications, Vol. 23, pp. 421–427, 1968.

    Article  Google Scholar 

  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. 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. 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. 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.

    Article  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  Google Scholar 

  20. M. T. Hagan, H. B. Demuth and M. Beale, Neural Network Design, PSW Publishing, 1996.

    Google Scholar 

  21. S. K. Pal and D. K. D. Majumder, Fuzzy Mathematical Approach to Pattern Recognition, John Wiley & Sons, 1986.

    Google Scholar 

  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. 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.

    Article  Google Scholar 

  24. R. C. Gonzalez and R. E. Woods, Digital Image Processing, 3rd Edition. Addison-Wesley, MA, 1992.

    Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  28. S. Haykin, Neural Networks — A Comprehensive Foundation, Macmillan College Publishing Company, Inc., 1994.

    Google Scholar 

  29. E. Gose, R. Johnsonbaugh, and S. Jost, Pattern Recognition and Image Analysis, Prentice Hall, New Jersey, 1996.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Utah State University, Logan, UT, 84322-4205, USA

    H. D. Cheng, Y. G. Hu & C. Y. Wu

  2. Department of Computer, Information and Systems Engineering, San Jose State University, San Jose, CA, 95192-0180, USA

    D. L. Hung

Authors
  1. H. D. Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Y. G. Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. D. L. Hung
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. C. Y. Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of electronics and Computer Science, University of Santiago de Compostela, 15782, Santiago de Compostela, Spain

    Senén Barro

  2. AI and Knowledge Engineering Group, School of Computer Science, University of Murcia, Campus de Espinardo, 30071, Murcia, Spain

    Roque Marín

Rights and permissions

Reprints and permissions

Copyright information

© 2002 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Cheng, H.D., Hu, Y.G., Hung, D.L., Wu, C.Y. (2002). Breast Cancer Classification Using Fuzzy Central Moments. In: Barro, S., Marín, R. (eds) Fuzzy Logic in Medicine. Studies in Fuzziness and Soft Computing, vol 83. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1804-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-7908-1804-8_3

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2498-8

  • Online ISBN: 978-3-7908-1804-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation