Automatic detection of clustered microcalcifications using a combined method and 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 filtering 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 false-positive reduction step we separate false signals from microcalcifications by means of a Support Vector Machine (SVM) classifier. Our algorithm yields a sensitivity of 95% with 0.6 false positive cluster per image on the 40 images of the Nijmegen database.

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