Breast cancer is one of the most common cancers among women. One of the early signs of the disease is the appearance of microcalcifications clusters, which often show up as bright spots in mammograms. It is important to be able to distinguish between the shapes of these clusters to increase the reliability and accuracy of the diagnosis. In this paper, a new method to extract features to classify the microcalcification clusters using steerable pyramid decomposition is presented. The method is motivated by the fact that microcalcification clusters can be of arbitrary sizes and orientations. Thus, it is important to extract the features in all possible orientations to capture most of the distinguishing information for classification. The proposed method shows a clear improvement in the classification performance when compared to the wavelet transform; the most commonly used multiscale analysis technique at present.