Because of ultrasound images' low quality, fully automated segmentation of breast ultrasound (BUS) image is a challenging task. In this paper, a novel segmentation method for BUS images which is fully automatic without any human intervention is proposed. By incorporating empirical knowledge and characteristics of breast structure, a ROI is generated automatically. Then two newly proposed lesion features: phase in max-energy orientation (PMO) and radial distance (RD), combined with the commonly used intensity and texture feature, are extracted. Then the new feature set is used to distinguish lesion region from the background by a trained ANN. The proposed segmentation method was tested on a BUS database composed of 60 cases. We use the manually outlined lesions by an experienced radiologist as the golden standard and evaluated the performance by both area error metrics and boundary error metrics. Quantitative results demonstrate the efficiency of the proposed fully automatic BUS segmentation method.