A Hybrid Framework for Tumor Saliency Estimation
This work addresses the problem of improving tumor detection in medical imaging for healthcare applications, but it appears incremental as it builds on existing tumor saliency estimation approaches.
The paper tackles the challenge of tumor segmentation in breast ultrasound images by proposing a hybrid framework for tumor saliency estimation that integrates domain-knowledge and saliency assumptions, and it outperforms state-of-the-art methods.
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.