Automatic Classification of Clustered Microcalcifications by a Multiple Classifier System

  • P. Foggia
  • C. Sansone
  • F. Tortorella
  • M. Vento
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2096)


Mammography is a not invasive diagnostic technique widely used for early detection of breast cancer. One of the main indicants of cancer is the presence of microcalcifications, i.e. small calcium accumulations, often grouped into clusters. Automatic detection and recognition of malignant clusters of microcalcifications are very difficult because of the small size of the microcalcifications and of the poor quality of the mammographic images. Up to now, mainly two kinds of approaches have been proposed to tackle this problem: those performing the classification by looking at the features of single microcalcifications and those based on the classifications of clusters, which in turn use features characterizing the spatial distribution of the microcalcification in the breast. In this paper we propose a novel approach for recognizing malignant clusters, based on a Multiple Classifier System (MCS) which uses simultaneously the evidences obtainable from the classification of the single microcalcifications and from the classification of the cluster considered as a whole. The approach has been tested on a standard database of 40 mammographic images and revealed very effective with respect to the single experts.


Receiver Operating Characteristic Curve Automatic Classification Mammographic Image Invasive Diagnostic Technique Single Expert 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    W.A. Murphy, K. DeSchryver-Kecskemeti, “Isolated clustered microcalcifications in the breast: radiologic-pathologic correlation”, Radiology, vol. 127, pp. 335–341, 1978.Google Scholar
  2. [2]
    M. Lanyi, Diagnosis and differential diagnosis of breast calcifications, Springer-Verlag, New York, 1986.Google Scholar
  3. [3]
    B. de Lafontan, J.P. Daures, B. Salicru, F. Eynius, J. Mihura, P. Rouanet, J.L. Lamarque, A. Naja, H. Pujol, “Isolated Clustered Microcalcifications: Diagnostic Value of Mammography-Series of 400 Cases with Surgical Verification”, Radiology, vol. 190, pp. 479–483, 1994.Google Scholar
  4. [4]
    E.D. Pisano, F. Shtern “Image processing and Computer Aided Diagnosis in digital mammography: A clinical perspective”, Int. Journal of Pattern Recognition and Artificial Intelligence, vol. 7,no. 6, pp. 1493–1503, 1993.CrossRefGoogle Scholar
  5. [5]
    A.P. Dhawan, Y. Chitre, C. Kaiser-Bonasso, M. Moskowitz, “Analysis of Mammographic Microcalcifications Using Gray-Level Image Structure Features”, IEEE Trans. on Medical Imaging, vol. 15,no. 3, pp. 246–259, 1996.CrossRefGoogle Scholar
  6. [6]
    M.J. Lado, P.G. Tahoces, A.J. Méndez, M. Souto, J.J. Vidal, “A Wavelet-based Algorithm for Detecting Clustered Microcalcifications in Digital Mammograms”, Medical Physics, vol. 26,no. 7, pp. 1294–1305, 1999.CrossRefGoogle Scholar
  7. [7]
    S.S. Buchbinder, I.S. Leichter, P.N. Bamberger, B. Novak, R. Lederman, S. Fields, D.J. Behar, “Analysis of Clustered Microcalcifications by Using a Single Numeric Classifier Extracted from Mammographic Digital Images”, Academic Radiology, vol. 5,no. 11, pp. 779–784, 1998.CrossRefGoogle Scholar
  8. [8]
    T.K Ho, J.J. Hull, S. Shrihari, “Decision Combination in Multiple Classifier Systems”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol 16,no.1, pp.66–75, 1994.CrossRefGoogle Scholar
  9. [9]
    J. Kittler, “A Framework for Classifier Fusion: Is It Still Needed?”, in F.J. Ferri, J.M. Iñesta, A. Amin, P. Pudil (eds.), Advances in Pattern Recognition, Lecture Notes in Computer Science 1876, pp. 45–56, Springer, Berlin, 2000.CrossRefGoogle Scholar
  10. [10]
    L. Shen, R.M. Rangayyan, J.E.L. Desautels, “Application of Shape Analysis to Mammographic Calcifications”, IEEE Trans. on Medical Imaging, vol. 13,no. 2, pp. 234–253, 1994.CrossRefGoogle Scholar
  11. [11]
    N. Karssemeijer, “Adaptive Noise Equalization and Recognition of Microcalcification Clusters in Mammograms”, Int. Journal of Pattern Recognition and Artificial Intelligence, vol. 7,no. 6, pp. 1357–1376, 1993.CrossRefGoogle Scholar
  12. [12]
    C.E. Metz, “ROC methodology in radiologic imaging”, Investigative Radiology, vol. 21, pp. 720–733, 1986.CrossRefGoogle Scholar
  13. [13]
    C.E. Metz, B.A. Herman, J-H. Shen, “Maximum-likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data”, Statistics in Medicine, vol. 17,no. 9, pp. 1033–53.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • P. Foggia
    • 1
  • C. Sansone
    • 1
  • F. Tortorella
    • 2
  • M. Vento
    • 1
  1. 1.Dipartimento di Informatica e SistemisticaUniversità degli Studi di Napoli “Federico II”NapoliItaly
  2. 2.Dipartimento di Automazione, Elettromagnetismo, Ingegneria dell’Informazione e Matematica IndustrialeUniversità degli Studi di CassinoCassinoItaly

Personalised recommendations