  Automatic tumor segmentation of breast ultrasound (BUS) image is quite
challenging due to the complicated anatomic structure of breast and poor image
quality. Most tumor segmentation approaches achieve good performance on BUS
images collected in controlled settings; however, the performance degrades
greatly with BUS images from different sources. Tumor saliency estimation (TSE)
has attracted increasing attention to solving the problem by modeling
radiologists' attention mechanism. In this paper, we propose a novel hybrid
framework for TSE, which integrates both high-level domain-knowledge and robust
low-level saliency assumptions and can overcome drawbacks caused by direct
mapping in traditional TSE approaches. The new framework integrated the
Neutro-Connectedness (NC) map, the adaptive-center, the correlation and the
layer structure-based weighted map. The experimental results demonstrate that
the proposed approach outperforms state-of-the-art TSE methods.
