Skip to main content

Detection of Microcalcifications in Digital Mammograms Based on Dual-Threshold

  • Conference paper
Digital Mammography (IWDM 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4046))

Included in the following conference series:

Abstract

Breast cancer is one of the main leading causes to women mortality in the world especially in the western countries. Since the causes are still unknown, breast cancer cannot be prevented completely even till now. Microcalcification clusters are primary indicators of malignant types of breast cancer, the detection is important to prevent and treat the disease. The microcalcifications appear in the small clusters of a few pixels with relatively high intensity and closed contours compared with their neighboring pixels. However, it is a challenge to detect all the microcalcifications since they appear as spots which are slightly brighter than their backgrounds. This paper presents an approach for detecting microcalcifications in digital mammograms employing a dual-threshold method. These microcalcifications can be located by our new method which is developed from LoG edge detection method. Two thresholds are proposed in our method based on two additional criterions. Experimental results show that the proposed method can locate the microcalcifications exactly in mammogram as well as restrain the contours produced by the noises.

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

Access this chapter

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

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. Dengler, J., Behrens, S., Desaga, J.: Segmentation of microcalcifications in mammograms. IEEE Transactions on Medical Imaging 12, 634–642 (1993)

    Article  Google Scholar 

  2. Davies, D.H., Dance, D.R.: Automatic computer detection of clustered calcifications in digital mammograms. Phys. Med. Biol. 35, 1111–1118 (1990)

    Article  Google Scholar 

  3. Davies, D.H., Dance, D.R., Jones, C.H.: Automatic detection of clusters of calcifiations. In: SPIE Med. Imaging IV: Image Process, vol. 1233, pp. 185–191 (1990)

    Google Scholar 

  4. Davies, D.H., Dance, D.R., Jones, C.H.: Automatic detection of microcalcifiations in digital mammograms using local area thresholding techniques. In: SPIE Med. Imaging III: Image Process., vol. 1092, pp. 153–157 (1989)

    Google Scholar 

  5. Netsch, T., Peitgen, H.: Scale space signatures for the detection of clustered microcalcifications in digital mammograms. IEEE Trans. Med. Imaging. 18, 774–786 (1999)

    Article  Google Scholar 

  6. Cheng, H.D., Lui, Y.M., Freimanis, R.I.: A novel approach to microcalcification detection using fuzzy logic technique. IEEE Trans. Med. Imaging. 17, 442–450 (1998)

    Article  Google Scholar 

  7. Zheng, B., Qian, W., Clarke, L.P.: Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcification. IEEE Trans. Med. Imaging. 15, 589–597 (1996)

    Article  Google Scholar 

  8. Meersman, D., Scheunders, P., Van Dyck, D.: Detection of microcalcification using neural networks. In: Digital Mammography 1996, pp. 287–290. Elsevier, Amsterdam (1996)

    Google Scholar 

  9. Astey, S., Hutt, I., Miller, P., Rose, P., Boggis, C., Adamson, S., Valentine, T., Davies, J., Rmstrong, J.: Automation in mammography: computer vision and human perception. Int. J. of pattern Recog. And Art. Intt. 7, 1313–1338 (1993)

    Article  Google Scholar 

  10. Ulupinar, F., Medioni, G.: Refining edges detected by a LoG operator. Computer Vision and Pattern Recognition. In: Proceedings CVPR 1988, Computer Society Conference, pp. 202–207 (1988)

    Google Scholar 

  11. Liqin, S., Dinggang, S., Feihu, Q.: Edge detection on real time using LOG filter. In: Speech, Image Processing and Neural Networks, Proceedings, ISSIPNN 1994, International Symposium, vol. 1, pp. 37–40 (1994)

    Google Scholar 

  12. Marr, D., Hildreth, E.: Theory of edge detection. Proceedings if the Royal Society, 187–217 (1980)

    Google Scholar 

  13. http://peipa.essex.ac.uk/ipa/pix/mias/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Wu, Y., Huang, Q., Peng, Y., Situ, W. (2006). Detection of Microcalcifications in Digital Mammograms Based on Dual-Threshold. In: Astley, S.M., Brady, M., Rose, C., Zwiggelaar, R. (eds) Digital Mammography. IWDM 2006. Lecture Notes in Computer Science, vol 4046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11783237_47

Download citation

  • DOI: https://doi.org/10.1007/11783237_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-35625-7

  • Online ISBN: 978-3-540-35627-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics