A saliency model for automated tumor detection in breast ultrasound images

Tumor detection is the key issue of computer-aided diagnosis (CAD) systems using breast ultrasound (BUS) images. However, accurately and automatically locating the suspicious lesions in BUS images is still a very challenging job. In this paper, we propose a saliency model to describe the radiologists' visual (RV) attention. The main contributions of the paper are: (1) The saliency model is built based on biological hypotheses instead of using inflexible assumptions which are more robust, adaptive and objective. (2) Background based attention cue is also proposed. Before, the background information was either ignored or only used for excluding non-target areas. However, we find that the background information is very useful for establishing the anatomy constrains. The proposed method is evaluated using real breast ultrasound images and the result demonstrates a significantly improved performance comparing with that of the state-of-the-art methods.