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.

• 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
• Keywords: Computer Aided Diagnosis, Detection Algorithm, ESANN 2000, ESANN 2007, Gaussianity Statistical Tests