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

Abstract

Human visual mechanisms (HVMs) can quickly localize the most salient object in natural images, but it is ineffective at localizing tumors in ultrasound breast images. In this paper, we research the characteristics of tumors, develop a classic HVM and propose a novel auto-localization method. Comparing to surrounding areas, tumors have higher global and local contrast. In this method, intensity, blackness ratio and superpixel contrast features are combined to compute a saliency map, in which a Winner Take All algorithm is used to localize the most salient region, which is represented by a circle. The results show that the proposed method can successfully avoid the interference caused by background areas of low echo and high intensity. The method has been tested on 400 ultrasound breast images, among which 376 images succeed in localization. This means this method has a high accuracy of 94.00%, indicating its good performance in real-life applications.

Tumor saliency estimation for breast ultrasound images via breast anatomy modeling

“…1. Many approaches [7,26,34] were proposed to model the visual cues attracting radiologists' attention. In [7], Shao et al proposed a model based on saliency estimation for fully automatic tumor detection.…”

“…1 (c)). Xie et al [26] computed tumor saliency by comprising intensity, blackness ratio, and superpixel contrast separately; and the average of the values of the three components was the final saliency value of each pixel. The drawbacks were shared as [7] due to the nature of direct mapping and the strategy of "winner-take-all".…”