The detection and segmentation of stellate lesions in mammograms is a difficult task in image processing due to the high variances in their appearance. We present the application of an interactive generic system, that is trained to detect and segment stellate lesions based on their local features. The training is done by an expert presenting examples of stellate lesions to the system. With the data available good detection results are achieved, yet the performance of the system can be increased as more examples are presented.