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Tumor saliency estimation aims to localize tumors by modeling the visualstimuli in medical images. However, it is a challenging task for breastultrasound due to the complicated anatomic structure of the breast and poorimage quality; and existing saliency estimation approaches only model genericvisual stimuli, e.g., local and global contrast, location, and featurecorrelation, and achieve poor performance for tumor saliency estimation. Inthis paper, we propose a novel optimization model to estimate tumor saliency byutilizing breast anatomy. First, we model breast anatomy and decompose breastultrasound image into layers using Neutro-Connectedness; then utilize thelayers to generate the foreground and background maps; and finally propose anovel objective function to estimate the tumor saliency by integrating theforeground map, background map, adaptive center bias, and region-basedcorrelation cues. The extensive experiments demonstrate that the proposedapproach obtains more accurate foreground and background maps with theassistance of the breast anatomy; especially, for the images having large orsmall tumors; meanwhile, the new objective function can handle the imageswithout tumors. The newly proposed method achieves state-of-the-art performancewhen compared to eight tumor saliency estimation approaches using two breastultrasound datasets.

Tumor Saliency Estimation for Breast Ultrasound Images via Breast Anatomy Modeling