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Automatic detection of clustered microcalcifications in digital mammograms using an SVM classifier

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Automatic detection of clustered microcalcifications in digital mammograms using an SVM classifier
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 on the combination of two different methods. The first one, based on difference-image techniques and gaussianity statistical tests, finds out the most obvious signals. The second one is able to discover more subtle microcalcifications by exploiting a multiresolution analysis by means of the wavelet transform. In the falsepositive reduction step we separate false signals from microcalcifications by means of an SVM classifier. Our algorithm yields a sensitivity of 94.6% with 0.6 false positive cluster per image on the 40 images of the Nijmegen database.
Armando Bazzani, Alessandro Bevilacqua, Dante Boll
Added 01 Nov 2010
Updated 01 Nov 2010
Type Conference
Year 2000
Where ESANN
Authors Armando Bazzani, Alessandro Bevilacqua, Dante Bollini, Rosa Brancaccio, Renato Campanini, Nico Lanconelli, Alessandro Riccardi, Davide Romani, Gianluca Zamboni
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