Skip to main content
SpringerLink
Log in

Mammographic Mass Detection Using Unsupervised Clustering in Synergy with a Parcimonious Supervised Rule-Based Classifier

  • 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:

  • 1451 Accesses

Abstract

We develop a novel CAD detection system that can help a radiologist to detect masses in mammograms. The proposed algorithm concurrently detects the breast boundary and the pectoral muscle. Then, a clustering and morphology based segmentation algorithm is applied to the enhanced mammography image to separate the mass from the normal breast tissues. This technique outlines the shape of candidate masses in mammograms. To maximize detection specificity, we develop a two-stage hybrid classification network. First, an unsupervised classifier is used to classify suspicious opacities as suspect or not. Then, a few supervised interpretation rules are applied to further reduce the number of false detections. Using a private mammography database and the publicly available USF/DDSM database, experimental results demonstrate that a sensitivity of 94% (resp. 80%) can be achieved at a specificity level of 1.02 (resp. 0.69) false positives per image. Even in dense mammograms, the CAD algorithm can still correctly detect subtle masses.

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

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. Allen, B.H., Oxley, M.E., Collins, M.J.: A universal segmentation platform for computer-aided detection. In: Peitgen, H.-O. (ed.) 6th International Workshop on Digital Mammography, Bremen, Germany, June 22-25, 2002, pp. 164–168. Springer, Heidelberg (2003)

    Google Scholar 

  2. Bin-Zheng, Yuan-Hsiang-Chang, Xiao-Hui-Wang, Good, W.F., Gur, D.: Application of a Bayesian belief network in a computer-assisted diagnosis scheme for mass detection. In: Medical Imaging 1999: Image Processing, Proceedings of the SPIE, San Diego, CA, USA, February 22-25, pp. 1553–1561, The International Society for Optical Engineering. vol. 3661 pt. 1-2 (1999)

    Google Scholar 

  3. Bruynooghe, M.: Maximal Theoretical Complexity of Fast Hierarchical Clustering Algorithms Based on the Reducibility Property. International Journal of Pattern Recognition and Artificial Intelligence 7(3), 541–571 (1993)

    Article  Google Scholar 

  4. te-Brake, G.M., Karssemeijer, N., Hendriks, J.: An automatic method to discriminate malignant masses from normal tissue in digital mammograms. Physics-in-Medicine-and-Biology 45(10), 2843–2857 (2000)

    Article  Google Scholar 

  5. Campanini, R., Dongiovanni, D., Iampieri, E., Lanconelli, N., Masotti, M., Palermo, G., Riccardi, A., Roffilli, M.: A novel featureless approach to mass detection in digital mammograms based on support vector machines. Physics-in-Medicine-and-Biology 49(6), 961–975 (2004)

    Article  Google Scholar 

  6. Cheng, H.D., Cui, M.: Mass lesion detection with a fuzzy neural network. Pattern-Recognition 37(6), 1189–1200 (2004)

    Article  Google Scholar 

  7. Gale, A., Mugglestone, M., Cowley, H., Wooding, D.: Human factors considerations for CAD implementation in breast screening. In: 5th International Workshop on Digital Mammography, Toronto, Canada, June 11-14, pp. 461–467 (2000)

    Google Scholar 

  8. Hatanaka, Y., Hara, T., Fujita, H., Kasai, S., Endo, T., Iwase, T.: Development of an automated method for detecting mammographic masses with a partial loss of region. IEEE-Transactions-on-Medical-Imaging 20(12), 1209–1214 (2001)

    Article  Google Scholar 

  9. Heath, M., Bowyer, K., Kopans, D., Moore, R., Kegelmeyer, P.: The Digital Database for Screening Mammography. In: 5th International Workshop on Digital Mammography, Toronto, Canada, June 11-14, pp. 212–218 (2000)

    Google Scholar 

  10. Heath, M., Bowyer, K.: Mass detection by relative image intensity. In: 5th International Workshop on Digital Mammography, Toronto, Canada, June 11-14, pp. 219–225 (2000)

    Google Scholar 

  11. Hong, B.W., Brady, M.: A topographic representation for mammogram segmentation. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 730–737. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  12. Khan, F., Sarma, A., Ying-Sun, Tufts, D.: Mass detection using tolerance intervals and a rank detector. In: IEEE International Symposium on Biomedical Imaging, Washington, DC, USA, July 7-10 (2002)

    Google Scholar 

  13. Kupinski, M.A., Giger, M.L.: Automated seeded lesion segmentation on digital mammograms. IEEE-Transactions-on-Medical-Imaging 17(4), 510–517 (1998)

    Article  Google Scholar 

  14. Paquerault, S., Petrick, N., Heang-Ping-Chan, Sahiner, B., Helvie, M.A.: Improvement of computerized mass detection on mammograms: Fusion of two-view information. Medical-Physics 29(2), 238–247 (2002)

    Article  Google Scholar 

  15. Lei-Zheng, Chan, A.K., McCord, G., Wu, S., Liu, J.S.: Detection of cancerous masses for screening mammography using discrete wavelet transform-based multiresolution Markov random field. Journal-of-Digital-Imaging 12(2), 18–23 (1999)

    Google Scholar 

  16. Lihua-Li, Yang-Zheng, Lei-Zhang, Clark, R.A.: False-positive reduction in CAD mass detection using a competitive classification strategy. Medical-Physics 28(2), 250–258 (2001)

    Google Scholar 

  17. Mudigonda, N.R., Rangayyan, R.M., Leo-Desautels, J.E.: Detection of breast masses in mammograms by density slicing and texture flow-field analysis. IEEE-Transactions-on-Medical-Imaging 20(12), 1215–1227 (2001)

    Article  Google Scholar 

  18. Petrick, N., Heang-Ping-Chan, Sahiner, B., Helvie, M.A.: Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms. Medical-Physics 26(8), 1642–1654 (1999)

    Article  Google Scholar 

  19. Sahiner, B., Petrick, N., Heang-ping-Chan, Paquerault, S., Helvie, M.A., Hadjiiski, L.M.: Recognition of lesion correspondence on two mammographic views-a new method of false-positive reduction for computerized mass detection. In: Medical Imaging 2001: Image Processing, Proceedings of the SPIE, San Diego, CA, USA, February 19-22, vol. 4322 pt. 1-3, pp. 649–655, The International Society for Optical Engineering (2001)

    Google Scholar 

  20. Timp, S., Karssemeijer, N., Hendricks, J.: Comparison of three different mass segmentation methods. In: Peitgen, H.-O. (ed.) 6th International Workshop on Digital Mammography, Bremen, Germany, June 22-25, pp. 218–222. Springer, Heidelberg (2002)

    Google Scholar 

  21. Yarlagadda, N., Bowyer, K., Li, R.: Baseline Comparison of Microcalcification Detection Algorithms. In: 5th International Workshop on Digital Mammography, Toronto, Canada, June 11-14, pp. 414–420 (2000)

    Google Scholar 

  22. Yates, K., Evans, C., Brady, M.: Improving the Brake’s mammographic mass-detection algorithm using phase congruency. In: Proceedings of the Sixth Digital Image Computing Techniques and Applications. Dicta 2002, pp. 179–183 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University Louis Pasteur of Strasbourg, Yorckstrasse 49, 76185, Karlsruhe, Germany

    Michel Bruynooghe

Authors
  1. Michel Bruynooghe
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Division of Imaging Science and Biomedical Engineering, Stopford Building, University of Manchester, Oxford Road, M13 9PT, Manchester

    Susan M. Astley

  2. Wolfson Medical Vision Lab, University of Oxford, UK

    Michael Brady

  3. Imaging Science and Biomedical Engineeering, University of Manchester, Oxford Road, M13 9PT, Manchester, UK

    Chris Rose

  4. Department of Computer Science, University of Wales, Aberystwyth, UK

    Reyer Zwiggelaar

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bruynooghe, M. (2006). Mammographic Mass Detection Using Unsupervised Clustering in Synergy with a Parcimonious Supervised Rule-Based Classifier. 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_10

Download citation

  • DOI: https://doi.org/10.1007/11783237_10

  • 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

Search

Navigation