Algorithms for Detecting Clusters of Microcalcifications in Mammograms

  • Claudio Marrocco
  • Mario Molinara
  • Francesco Tortorella
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

Abstract

Mammography is a not invasive diagnostic technique widely used for early cancer detection in women breast. A particularly significant clue of such disease is the presence of clusters of microcalcifications. The automatic detection of such clusters is a very difficult task because of the small size of the microcalcifications and of the poor quality of the digital mammograms. In literature, all the proposed method for the automatic detection focus on the single microcalcification. In this paper, an approach that moves the final decision on the regions identified by the segmentation in the phase of clustering is proposed. To this aim, the output of a classifier on the single microcalcifications is used as input data in different clustering algorithms which produce the final decision. The approach has been successfully tested on a standard database of 40 mammographic images, publicly available.

Keywords

Cluster Algorithm Ground Truth Cover Factor Sequential Algorithm Mammographic Image 
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.

References

  1. 1.
    De Santo, M., Molinara, M., Tortorella, F., Vento, M.: Automatic Classification of Clustered Microcalcifications by a Multiple Expert System. Pattern Recognition 36, 1467–1477 (2003)CrossRefGoogle Scholar
  2. 2.
    Karssemeijer, N.: Adaptive Noise Equalization and Recognition of Microcalcification Clusters in Mammograms. International Journal of Pattern Recognition and Artificial Intelligence 7, 1357–1376 (1993)CrossRefGoogle Scholar
  3. 3.
    Strickland, R.N., Hahn, H.: Wavelet Transforms for Detecting Microcalcifications in Mammograms. IEEE Transaction on Medical Imaging 15, 218–229 (1996)CrossRefGoogle Scholar
  4. 4.
    Netsch, T., Peitgen, H.: Scale Space Signatures for the Detection of Clustered Microcalcifications in Digital Mammograms. IEEE Transaction on Medical Imaging 18, 774–786 (1999)CrossRefGoogle Scholar
  5. 5.
    Cheng, H.D., Lui, Y.M., Freimanis, R.I.: A Novel Approach to Microcalcification Detection Using Fuzzy Logic Technique. IEEE Transaction on Medical Imaging 17, 442–450 (1998)CrossRefGoogle Scholar
  6. 6.
    Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 2nd edn. Elsevier Science, Amsterdam (2003)Google Scholar
  7. 7.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. John Wiley & Sons, Inc., Chichester (2001)MATHGoogle Scholar
  8. 8.
    D’Elia, C., Marrocco, C., Molinara, M., Poggi, G., Scarpa, G., Tortorella, F.: Detection of Microcalcifications Clusters in Mammograms through TS-MRF Segmentation and SVM-based Classification. In: Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Claudio Marrocco
    • 1
  • Mario Molinara
    • 1
  • Francesco Tortorella
    • 1
  1. 1.Dipartimento di Automazione, Elettromagnetismo, Ingegneria dell’Informazione e Matematica IndustrialeUniversità degli Studi di CassinoCassinoItaly

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