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)


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.


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.


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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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