A Graph-Based Method for Detecting and Classifying Clusters in Mammographic Images

  • P. Foggia
  • M. Guerriero
  • G. Percannella
  • C. Sansone
  • F. Tufano
  • M. Vento
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4109)


In this paper we propose a method based on a graph-theoretical cluster analysis for automatically finding and classifying clusters of microcalcifications in mammographic images, starting from the output of a microcalcification detection phase. This method does not require the user to provide either the expected number of clusters or any threshold values, often with no clear physical meaning, as other algorithms do.

The proposed approach has been tested on a standard database of 40 mammographic images and has demonstrated to be very effective, even when the detection phase gives rise to several false positives.


Minimum Span Tree Multi Layer Perceptron Clear Physical Meaning Cluster Classification Cluster Detection 
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.


  1. 1.
    Lanyi, M.: Diagnosis and differential diagnosis of breast calcifications. Springer, New York (1986)Google Scholar
  2. 2.
    De Yoldi, G.C., Viganotti, G., Bergonzi, S., Gerranti, C., Piragine, G., Cassano, E., Barberini, M., Rilke, F., Veronesi, U.: Le microcalcificazioni nei carcinomi mammari non palpabili. Analisi di 427 casi (in Italian), Rad. Med., no. 85, pp. 611–614 (1993)Google Scholar
  3. 3.
    Lauria, A., Palmiero, R., Imbriaco, M., Selva, G., et al.: Analysis of radiologist performance with and without a CAD system. In: European Congress of Radiology (2002)Google Scholar
  4. 4.
    Cheng, H.D., Cai, X., Chen, X., Hu, L., Lou, X.: Computer-aided detection and classification of microcalcifications in mammograms: a survey. International Journal on Pattern Recognition 36, 2967–2991 (2003)MATHCrossRefGoogle Scholar
  5. 5.
    Thangavel, K., Karnan, M., Sivakumar, R., Kaja Mohideen, A.: Automatic Detection of Microcalcification in Mammograms – A Review. International Journal on Graphics, Vision and Image Processing 5, 31–61 (2005)Google Scholar
  6. 6.
    Zahn, C.T.: Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers 20(1), 68–86 (1971)MATHCrossRefGoogle Scholar
  7. 7.
    Horowitz, E., Sahni, S.: Fundamentals of Computer Algorithms. Computer Science Press (1978)Google Scholar
  8. 8.
    Karssemeijer, N.: Adaptive Noise Equalization and Recognition of Microcalcification Clusters in Mammograms. Int. Journal of Patt. Rec. and Artificial Intelligence 7(6), 1357–1376 (1993)CrossRefGoogle Scholar
  9. 9.
    Sajda, P., Spence, C., Pearson, J.: Learning contextual relationships in mammograms using a hierarchical pyramid neural network. IEEE Transactions on Medical Imaging 21(3), 239–250 (2002)CrossRefGoogle Scholar
  10. 10.
    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: IEEE International Conference on Pattern Recognition, vol. 3, pp. 742–745 (2004)Google Scholar
  11. 11.
    Netsch, T., Peitgen, H.: Scale-Space Signatures for the Detection of Clustered Microcalcifications in Digital Mammograms. IEEE Trans. on Medical Imaging 18(9), 774–786 (1999)CrossRefGoogle Scholar
  12. 12.
    Cheng, H.D., Wang, J., Shi, X.: Microcalcification Detection Using Fuzzy Logic and Scale Space Approach. Pattern Recognition 37, 363–375 (2004)MATHCrossRefGoogle Scholar
  13. 13.
    Yu, S., Guan, L.: A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films. IEEE Transactions on Medical Imaging 19(2), 115–126 (2000)CrossRefGoogle Scholar
  14. 14.
    Papadopoulos, A., Fotiadis, D.I., Likas, A.: Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artificial Intelligence in Medicine (in press, 2006)Google Scholar
  15. 15.
    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
  16. 16.
    Verma, B., Zakos, J.: A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Transactions on Inform. Technol. Biomed. 5(1), 46–54 (2001)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • P. Foggia
    • 1
  • M. Guerriero
    • 2
  • G. Percannella
    • 2
  • C. Sansone
    • 1
  • F. Tufano
    • 2
  • M. Vento
    • 2
  1. 1.Dipartimento di Informatica e SistemisticaUniversità di Napoli “Federico II”NapoliItaly
  2. 2.Dipartimento di Ingegneria dell’Informazione e di Ingegneria ElettricaUniversità di SalernoFisciano (SA)Italy

Personalised recommendations