SYSTEM FOR AUTOMATIC DETECTION OF CLUSTERED MICROCALCIFICATIONS IN DIGITAL MAMMOGRAMS

A. Bazzani, D. Bollini, R. Brancaccio, R. Campanini, N. Lanconelli, D. Romani and A. Bevilacqua ()
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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;

International Journal of Modern Physics C (IJMPC), 2000, vol. 11, issue 05, 901-912

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.

Keywords: Computer Aided Diagnosis; Digital Mammograms; Image Processing; Microcalcifications; Wavelet; Pattern Recognition; CAD (search for similar items in EconPapers)
Date: 2000
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DOI: 10.1142/S0129183100000808

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