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Optimization Of A Distributed Genetic Algorithm On A Cluster Of Workstations For The Detection Of Microcalcifications

Author

Listed:
  • A. BEVILACQUA

    (Department of Electronics, Computer Science and Systems, University of Bologna, viale Risorgimento 2, 40136 Bologna, Italy;
    National Institute for Nuclear Physics (INFN) viale Berti Pichat 6/2, 40127 Bologna, Italy)

  • R. CAMPANINI

    (Department of Physics, University of Bologna and National Institute for Nuclear Physics (INFN) viale Berti Pichat 6/2, 40127 Bologna, Italy)

  • N. LANCONELLI

    (Department of Physics, University of Bologna and National Institute for Nuclear Physics (INFN) viale Berti Pichat 6/2, 40127 Bologna, Italy)

Abstract

We have developed a method for the detection of clusters of microcalcifications in digital mammograms. Here, we present a genetic algorithm used to optimize the choice of the parameters in the detection scheme. The optimization has allowed the improvement of the performance, the detailed study of the influence of the various parameters on the performance and an accurate investigation of the behavior of the detection method on unknown cases. We reach a sensitivity of 96.2% with 0.7 false positive clusters per image on the Nijmegen database; we are also able to identify the most significant parameters. In addition, we have examined the feasibility of a distributed genetic algorithm implemented on a non-dedicated Cluster Of Workstations. We get very good results both in terms of quality and efficiency.

Suggested Citation

  • A. Bevilacqua & R. Campanini & N. Lanconelli, 2001. "Optimization Of A Distributed Genetic Algorithm On A Cluster Of Workstations For The Detection Of Microcalcifications," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 55-70.
    • Handle: RePEc:wsi:ijmpcx:v:12:y:2001:i:01:n:s0129183101001523
    DOI: 10.1142/S0129183101001523
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