  Tumor saliency estimation aims to localize tumors by modeling the visual
stimuli in medical images. However, it is a challenging task for breast
ultrasound due to the complicated anatomic structure of the breast and poor
image quality; and existing saliency estimation approaches only model generic
visual stimuli, e.g., local and global contrast, location, and feature
correlation, and achieve poor performance for tumor saliency estimation. In
this paper, we propose a novel optimization model to estimate tumor saliency by
utilizing breast anatomy. First, we model breast anatomy and decompose breast
ultrasound image into layers using Neutro-Connectedness; then utilize the
layers to generate the foreground and background maps; and finally propose a
novel objective function to estimate the tumor saliency by integrating the
foreground map, background map, adaptive center bias, and region-based
correlation cues. The extensive experiments demonstrate that the proposed
approach obtains more accurate foreground and background maps with the
assistance of the breast anatomy; especially, for the images having large or
small tumors; meanwhile, the new objective function can handle the images
without tumors. The newly proposed method achieves state-of-the-art performance
when compared to eight tumor saliency estimation approaches using two breast
ultrasound datasets.
