Computer-Aided Detection of Microcalcifications in Digital Mammograms to Support Early Diagnosis of Breast Cancer

  • Nayid Triana
  • Alexander Cerquera
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7930)


Early detection of microcalcifications in mammograms is considered one of the best tools to prevent breast cancer. Although traditionally this task have been performed with analog mammograms, digital mammograms are currently an alternative for examination of breast to detect microcalcifications and any other kind of breast abnormalities. Digital mammography presents some advantages in comparison to its analog counterpart, such as lower radiation dosage for acquisition and possibility to storage for telemedicine purposes. Nevertheless, digitalization entails loss of resolution and difficulties to detect microcalcifications. Therefore, several methods based on digital image processing have been proposed to perform detection of microcalcifications in digital mammograms, to support the early detection and prognosis of breast cancer. However, sometimes computer-aided methods fail due to the characteristics of certain microcalcifications that are hard to detect either by visual examination and by computerized analysis. For this reason, this work presents a method based on contrast enhancement and wavelet reconstruction oriented to increase the rate of computer-aided detected microcalcifications. The images correspond to the mini-MIAS database, which provides mammograms of healthy women and with breast microcalcifications, including the respective coordinates of their locations. The work includes also the application of the method in resolution-enhanced mammograms via sparse representation, with the aim to determine the role of resolution enhancement for a possible improvement in the performance of the method.


Contrast Enhancement Sparse Representation True Positive Rate Digital Mammography Resolution Enhancement 
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.


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  1. 1.
    Nguyen, Fleiszer, D.: Interactive mammography analysis web tutorial. McGill Medicine. Molson Medical Informatics (2002),
  2. 2.
    Radiology - TIP, Xeromammography, Radiology - Technology Information Portal (2012),
  3. 3.
    Cuttino, J.T., Yankaskas, B.C., Hoskins, E.O.L.: Screen film mammography versus xeromammography in the detection of breast cancer. British Journal of Radiology 59, 1159–1162 (2005), CrossRefGoogle Scholar
  4. 4.
    AAPM, Equipment characteristics for xeromammography and screen-mammography. American Association of Physicists in Medicine (129) (1990)Google Scholar
  5. 5.
    Nishikawa, R.M., Giger, M.L., Doi, K., Vyborny, C.J., Schmidt, R.A.: Computer-aided detection of clustered microcalcifications on digital mammograms. Med. Biol. Eng. Comput. 33, 174–178 (1995)CrossRefGoogle Scholar
  6. 6.
    Gurcan, M.N., Yardimci, Y., Cetin, A.E., Ansari, R.: Detection of microcalcifications in mammograms using higher order statistics. IEEE Signal Process. Lett. 4, 213–216 (1997)CrossRefGoogle Scholar
  7. 7.
    Tang, J., Rangayyan, R.M., Xu, J., El Naqa, I., Yang, Y.: Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances. IEEE Trans. on Inf. Tech. in Biom. 13, 236–251 (2009)CrossRefGoogle Scholar
  8. 8.
    Regentova, E., Zhang, L., Zheng, J., Veni, G.: Microcalcification detection based on wavelet domain hidden Markov tree model: Study for inclusion to computer aided diagnostic prompting system. Med. Phys. 34, 2206–2219 (2007)CrossRefGoogle Scholar
  9. 9.
    Jiang, J., Yao, B., Wason, A.M.: A genetic algorithm design for microcalcification detection and classification in digital mammograms. Comput. Med. Imag. Graph. 31, 49–61 (2007)CrossRefGoogle Scholar
  10. 10.
    Peng, Y., Yao, B., Jiang, J.: Knowledge-discovery incorporated evolutionary search for microcalcification detection in breast cancer diagnosis. Artif. Intell. Med. 37, 43–53 (2006)CrossRefGoogle Scholar
  11. 11.
    Suckling, J., Boggis, C.R.M., Hutt, I., Astley, S., Betal, D., Cerneaz, N., Dance, D.R., Kok, S.L., Parker, J., Ricketts, I., Savage, J., Stamatakis, E., Taylor, P.: The Mammographic Image Analysis Society Digital Mammogram Database. In: Excerpta Medica, MiniMammography Database. International Congress Series 1069, pp. 375–378 (1994),
  12. 12.
    Yang, J., Wright, J., Huang, T., Ma, Y.: Image Super-Resolution via Sparse Representation. Trans. Img. Proc. 19, 2861–2873 (2010)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Hou, H.S., Andrews, H.C.: Cubic spline for image interpolation and digital filtering. IEEE Transactions on Signal Processing 26, 508–517 (1978)zbMATHGoogle Scholar
  14. 14.
    Leiner, B.J., Lorena, V.Q., Cesar, T.M., Lorenzo, M.V.: Microcalcifications Detection System through Discrete Wavelet Analysis and Contrast Enhancement Techniques. In: Electronics, Robotics and Automotive Mechanics Conference, CERMA 2008, pp. 272–276 (2008)Google Scholar
  15. 15.
    Pajares, J.G.: Visión por computador Imágenes digitales y aplicaciones, pp. 47–56. Alfaomega, Ra-Ma (2004)Google Scholar
  16. 16.
    Laine, A.F., Schuler, S., Fan, J., Huda, W.: Mammographic feature enhancement by multiscale analysis. IEEE Trans. Med. Imag. 13, 725–740 (1994)CrossRefGoogle Scholar
  17. 17.
    Gutiérrez, R.M., Cerquera, E.A., Mañana, G.: MPGD for breast cancer prevention: a high resolution and low dose radiation medical imaging. Journal of Instrumentation 7, C07007 (2012), doi:10.1088/1748-0221/7/07/C07007Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Nayid Triana
    • 1
  • Alexander Cerquera
    • 1
  1. 1.Faculty of Electronic and Biomedical Engineering, Complex Systems Research GroupAntonio Nariño UniversityBogotaColombia

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