Automatic Localization Algorithm for Ultrasound Breast Tumors Based on Human Visual Mechanism

Human visual system (HVM) can quickly localize the most salient object in scenes, which has been widely applied on natural image recognition [15]-[19]. In ultrasound (US) breast images, compared with surrounding areas, tumor is more salient because of its higher contrast. In this paper, we develop a novel automatic localization method based on HVM for automatic segmentation of US breast tumors. First intensity, blackness ratio, and superpixel contrast features are combined to compute saliency map, in which Winner Take All algorithm is used to localize the most salient region, presenting with a circle on the localized target. Finally the circle is taken as the initial contour of CV level set to finish the extraction of breast tumor. Combined with the proposed method, CV level set can achieve the fully automatic segmentation of US breast tumors. By combing intensity, blackness ratio and superpixel contrast features, the proposed localization method can successfully avoid the interference caused by background areas with low echo and high intensity. The localization method has been tested on 400 US beast images, among which 378 images succeed in localization, with high accuracy 92.00%.