Automatic segmentation of breast lesions for interaction in ultrasonic computer-aided diagnosis

Breast cancer is one of the most commonly diagnosed cancer types among women. Sonography has been regarded as an important imaging modality for diagnosis of breast lesions. Due to the speckle and the variance in shape and appearance of sonographic lesions, fully automatic segmentation of the breast tumor regions still remains a challenging task. In this paper, we propose an automatic interaction scheme based on an object recognition method to segment the lesions in breast ultrasound images. In this scheme, a 2D ultrasound image is firstly filtered with a total-variation model to reduce the speckle noise. A robust graph-based segmentation method is then used to segment the image into a number of sub-regions. An object recognition method incorporating the procedures of image feature extraction, feature selection and classification is proposed to automatically identify the regions which are associated with breast tumors. Finally, an active contour model is used to refine the contours of the regions that are recognized as tumors. This scheme is validated on a database of 46 breast ultrasound images with diagnosed tumors. The experimental results show that our scheme can segment the breast ultrasound images automatically, indicating its good performance in real applications.