The Spiral Method Applied to the Study of the Microcalcifications in Mammograms

  • Sergio Vitulano
  • Andrea Casanova
  • Valentina Savona
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)


In this paper a linear transformation, the spiral method, is introduced; this transformation maps an image represented in a 3-D space into a signal in a 2-D space. Some features of the spiral are presented: for instance the topologic information of the objects in the image, their contours, areas and the shape of the objects themselves. Two different case-study are presented: the use of spiral method in order to evaluate the number, the size, the shape and the location of the microcalcifications by the use of signals related to the mammograms; entropy is proposed as a measure of the degree of the parenchyma disorder of the mammograms and its use for a system CAD.


Topologic Information Knowledge Engineer Digital Mammogram Digital Database Grid 25x25 
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.
    Strickland, R.N.: Wavelet transforms for dectecting microcalcification in mammograms. IEEE Trans. On medical imaging 15(2) (April 1996)Google Scholar
  2. 2.
    Zhao, Q., et al.: Multi-resolution source coding using Entropy constrained Dithered Scalar Quantization. In: Prooc. Of DCC 2004. IEEE, Los Alamitos (2004)Google Scholar
  3. 3.
    Toews, M., Arbel, T.: Entropy of likelihood feature selection for image correspondence. In: Prooc. ICCV 2003. IEEE, Los Alamitos (2003)Google Scholar
  4. 4.
    Heath, M., et al.: Current status of digital database for screening mammography, pp. 457–460. Kluwer Academic Pub., Dordrecht (1998)Google Scholar
  5. 5.
    Melloul, M., Joskowicz, L.: Segmentation of microcalcification in X-ray mammograms using entropy thresholding. In: Lemke, H.U., et al. (eds.) CARS 2002 (2002)Google Scholar
  6. 6.
    Ullman, J.D.: Principle of database and knowledge-based system. Computer Science Press (1989)Google Scholar
  7. 7.
    Petrakis, E.G.M., et al.: Similarity searching in medical image databases. IEEE Trans. Knowledge and Data Eng. 9, 435–447 (1997)CrossRefGoogle Scholar
  8. 8.
    Casanova, A., Vitulano, S.: Entropy As a Feature In The Analysis And Classification Of Signals. Series on Software Engineering and Knowledge Engineering, vol. 15. World Scientific, Singapore ISBN 981-256-137-4Google Scholar
  9. 9.
    Casanova, A., Fraschini, M.: HER: Application on Information Retrieval. Series on Software Engineering and Knowledge Engineering, vol. 15, pp. 150–159 (June 2003) ISBN:981-238-587-8Google Scholar
  10. 10.
    Casanova, A., Savona, V., Vitulano, S.: The Role of Entropy In Signal Analysis And Classification: An Application to Breast Diagnosis. In: Medicon2004 Ischia (2004)Google Scholar
  11. 11.
    Casanova, A., Di Gesù, V., Lo Bosco, G., Vitulano, S.: Entropy measures in image classification. In: 4rd International Workshop Hmp 2004: Human And Machine Perception (Santa Caterina di Pittinuri ) Italy, September 2004. Series on Software Engineering and Knowledge Engineering, World Scientific, Singapore (2004) (in press)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Sergio Vitulano
    • 1
  • Andrea Casanova
    • 1
  • Valentina Savona
    • 1
  1. 1.Dipartimento di Scienze Mediche InternisticheUniversità di CagliariCagliariItaly

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