In breast cancer field, researchers aim to automatically discriminate between benign and malignant masses in order to assist radiologists. In general, benign masses have smoothed contours, whereas, malignant tumors have spiculated boundaries. In this context, finding the adequate description remains a real challenge due to the complexity of mass boundaries. In this paper, we propose a novel shape descriptor named the Protuberance Selection (PS) based on depression and protuberance detection. This descriptor allows a good characterization of lobulations and spiculations in mass boundaries. Furthermore, it ensures invariance to geometric transformations. Experimental results show that the specified descriptor provides a promising classification performance. Also, results confirm that the new PS descriptor outperforms several shape features commonly used in breast cancer domain.