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System For Automatic Detection Of Clustered Microcalcifications In Digital Mammograms

Author

Listed:
  • A. BAZZANI

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

  • D. BOLLINI

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

  • R. BRANCACCIO

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

  • R. CAMPANINI

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

  • N. LANCONELLI

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

  • D. ROMANI

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

  • A. BEVILACQUA

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

Abstract

In this paper, we investigate the performance of a Computer Aided Diagnosis (CAD) system for the detection of clustered microcalcifications in mammograms. Our detection algorithm consists of the combination of two different methods. The first, based on difference-image techniques and gaussianity statistical tests, finds out the most obvious signals. The second, is able to discover more subtle microcalcifications by exploiting a multiresolution analysis by means of the wavelet transform. We can separately tune the two methods, so that each one of them is able to detect signals with similar features. By combining signals coming out from the two parts through a logical OR operation, we can discover microcalcifications with different characteristics. Our algorithm yields a sensitivity of 91.4% with 0.4 false positive cluster per image on the 40 images of the Nijmegen database.

Suggested Citation

  • A. Bazzani & D. Bollini & R. Brancaccio & R. Campanini & N. Lanconelli & D. Romani & A. Bevilacqua, 2000. "System For Automatic Detection Of Clustered Microcalcifications In Digital Mammograms," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 11(05), pages 901-912.
  • Handle: RePEc:wsi:ijmpcx:v:11:y:2000:i:05:n:s0129183100000808
    DOI: 10.1142/S0129183100000808
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