Skip to main content

Advertisement

Log in

A Method for Microcalcifications Detection in Breast Mammograms

  • Image & Signal Processing
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Breast cancer is the most cause of death for women above age 40 around the world. In this paper, we propose a method to detect microcalcifications in digital mammography images using two-dimensional Discrete Wavelets Transform and image enhancement techniques for removing noise as well as to get a better contrast. The initial step is applying a preprocessing techniques to improve the edge of the breast and then segmentation process (Region of interest) for eliminating some regions in the image, which are not useful for the mammography interpretation. Then unsharp masking and histogram modification technique has used to enhance the contrast of the image and to clarify some details like microcalcifications. Lastly, Discrete Wavelets Transform applied for detecting the abnormality. The proposed method has evaluated using the Mammographic Image Analysis Society (AS) mammography databases. The proposed method has achieved acceptable results.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 8
Fig. 9
Fig. 6
Fig. 7
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. American Cancer Society, Breast cancer. American Cancer Society, Atlanta, Ga, 2011.

    Google Scholar 

  2. Zimmerman, B.T., Understanding breast cancer genetics. University Press of Mississippi, United States of America, 2004.

    Google Scholar 

  3. B. E. Denarie, Using SURF imaging for efficient detection of micro-calcifications. M.Sc. Thesis, Department of Engineering Cybernetics, Mathematics and Electrical Engineering, Faculty of Information Technology, Norwegian University of Science and Technology, Norway, 2010.

  4. Oliver, A., Torrent, X., Lladó, M., and Tortajada, L., Tortajada. Automatic microcalcifications and cluster detection for digital and digitised mammograms. Knowledge-Based Systems. 28:68–75, 2012.

    Google Scholar 

  5. Sankar, D., and Thomas, T., A new fast fractal modeling approach for the detection of microcalcifications in mammograms. J Digit Imaging. 23(5):538–546, 2010.

    Article  PubMed  Google Scholar 

  6. N. Uchiyama, and M. Nascimento, Mammography-recent advances. InTech, 2012.

  7. Li, H., Liu, R., and Lo, S., Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms. IEEE Trans on Medical Imaging. 16(6):785–798, 1997.

    Article  CAS  PubMed  Google Scholar 

  8. Kalra, P.K., and Kumar, N., A novel automatic microcalcifications detection technique using Tsallis entropy & a type II fuzzy index. Computers & Mathematics with Applications. 60(8):2426–2432, 2010.

    Article  Google Scholar 

  9. Jiang, J., Yao, B., and Wason, A.M., A genetic algorithm design for microcalcifications detection and classification in digital mammograms. Computerized Medical Imaging and Graphics. 31(1):49–61, 2007a.

    Article  CAS  PubMed  Google Scholar 

  10. Cheng, H.D., Cai, X., Chen, X., Hu, L., and Lou, X., Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern recognition. 36(12):2967–2991, 2003.

    Article  Google Scholar 

  11. Yu, S., and Guan, L., A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE transactions on medical imaging. 19(2):115–126, 2000.

    Article  CAS  PubMed  Google Scholar 

  12. N. Karssemeijer, A stochastic model for automated detection of calcifications in digital mammograms. in Proc. 12th Int. Conf. Information Processing Medical Imaging, Wye, U.K., 1991, 227–238.

  13. N. Karssemeijer, Recognition of clustered microcalcifications using a random field model, biomedical image processing and biomedical visualization. In SPIE Proc., vol. 1905, San Jose, CA, 1993, 776–786.

  14. Rizzi, M., D'Aloia, M., and Castagnolo, B., Computer aided detection of microcalcifications in digital mammograms adopting a wavelet decomposition. Integrated Computer-Aided Engineering. 16(2):91–103, 2009.

    Google Scholar 

  15. Yu, S.-N., Li, K.-Y., and Huang, Y.-K., Detection of microcalcifications in digital mammograms using wavelet filter and Markov random field model. Computerized Medical Imaging and Graphics. 30(3):163–173, 2006a.

    Article  PubMed  Google Scholar 

  16. Yu, S.N., Li, K.Y., and Huang, Y.K., Detection of microcalcifications in digital mammograms using wavelet filter and Markov random field model. Computerized Medical Imaging and Graphics. 30(3):163–173, 2006b.

    Article  PubMed  Google Scholar 

  17. Jiang, J., Yao, B., and Wason, A.M., A genetic algorithm design for microcalcifications detection and classification in digital mammograms. Computerized medical imaging and graphics. 31(1):49–61, 2007b.

    Article  CAS  PubMed  Google Scholar 

  18. Thangavel, K., Karnan, M., Sivakumar, R., and Mohideen, A., Automatic detection of Microcalcification in mammograms– a review. ICGST International Journal on Graphics, Vision and Image Processing, pp. 31–61, 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abbas H. Hassin Alasadi.

Additional information

This article is part of the Topical Collection on Image & Signal Processing

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alasadi, A.H.H., Al-Saedi, A.K.H. A Method for Microcalcifications Detection in Breast Mammograms. J Med Syst 41, 68 (2017). https://doi.org/10.1007/s10916-017-0714-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-017-0714-7

Keywords

Navigation