The three primary signs for which radiologists search when screening mammograms for breast cancer are stellate lesions, microcalcifications, and circumscribed lesions. Stellate lesions are of particular importance, as they are almost always associated with a malignancy. Further, they are often indicated only by subtle architectural distortions and so are in general easier to miss than the other signs. We have developed a method for the automatic detection of stellate lesions in digitized mammograms, and have tested them on image data where the presence or absence of malignancies is known. We extract image features from the known images, use them to grow binary decision trees, and use those trees to label each pixel of new mammograms with its probability of being located on an abnormality. The primary feature for the detection of stellate lesions is ALOE, analysis of local oriented edges, which is derived from an analysis of the histogram of edge orientations in local windows. Other features, based on the Laws texture energy measures, have been developed to respond to normal tissue, and so improve the false alarm performance of the entire system.