  A computer-aided detection (CADe) system for the identification of
microcalcification clusters in digital mammograms has been developed. It is
mainly based on the application of wavelet transforms for image filtering and
neural networks for both the feature extraction and the classification
procedures.
  This CADe system is easily adaptable to different databases. We report and
compare the FROC curves obtained on the private database we used for developing
the CADe system and on the publicly available MIAS database. The results
achieved on the two databases show the same trend, thus demonstrating the good
generalization capability of the system.
