A supervised micro-calcification detection approach in digitised mammograms

We present in this paper a supervised approach for automatic detection of micro-calcifications. The system is based on learning the different morphology of the micro-calcifications using local features, which are extracted using a bank of filters. Afterwards, this set of features is used to train a pixel-based boosting classifier which at each round automatically selects the most salient one. Therefore, when a new mammogram is tested only the salient features are computed and used to classify each pixel of the mammogram as being part of a micro-calcification or actually being normal tissue. The experimental results shows the validity of our approach. Moreover, the robustness of our method is also demonstrated using a digitised database for the learning process and a different one for the testing, providing satisfactory results.