  In mammography, the efficacy of computer-aided detection methods depends, in
part, on the robust localisation of micro-calcifications ($\mu$C). Currently,
the most effective methods are based on three steps: 1) detection of individual
$\mu$C candidates, 2) clustering of individual $\mu$C candidates, and 3)
classification of $\mu$C clusters. Where the second step is motivated both to
reduce the number of false positive detections from the first step and on the
evidence that malignancy depends on a relatively large number of $\mu$C
detections within a certain area. In this paper, we propose a novel approach to
$\mu$C detection, consisting of the detection \emph{and} classification of
individual $\mu$C candidates, using shape and appearance features, using a
cascade of boosting classifiers. The final step in our approach then clusters
the remaining individual $\mu$C candidates. The main advantage of this approach
lies in its ability to reject a significant number of false positive $\mu$C
candidates compared to previously proposed methods. Specifically, on the
INbreast dataset, we show that our approach has a true positive rate (TPR) for
individual $\mu$Cs of 40\% at one false positive per image (FPI) and a TPR of
80\% at 10 FPI. These results are significantly more accurate than the current
state of the art, which has a TPR of less than 1\% at one FPI and a TPR of 10\%
at 10 FPI. Our results are competitive with the state of the art at the
subsequent stage of detecting clusters of $\mu$Cs.
