Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 5 April 2017 / Approved: 5 April 2017 / Online: 5 April 2017 (15:50:40 CEST)

A peer-reviewed article of this Preprint also exists.

Xie, Y.; Chen, K.; Lin, J. An Automatic Localization Algorithm for Ultrasound Breast Tumors Based on Human Visual Mechanism. Sensors 2017, 17, 1101. Xie, Y.; Chen, K.; Lin, J. An Automatic Localization Algorithm for Ultrasound Breast Tumors Based on Human Visual Mechanism. Sensors 2017, 17, 1101.

Journal reference: Sensors 2017, 17, 1101
DOI: 10.3390/s17051101

Abstract

Human visual system (HVM) can quickly localize the most salient object in scenes, which has been widely applied on natural image segmentation [15]-[19]. In ultrasound (US) breast images, compared with background 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 ultrasound (US) breast tumors. First, the input image is smoothed by convolution with a linearly separable Gaussian filter and then subsampled into a 9-layer Gaussian pyramid. Then 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. The localization method has been tested on 400 US beast images, among which 378 images have higher saliency than background areas and succeed in localization, with high accuracy 92.00%. The HVM localization method can be used to localize the tumors, combined with this 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. Moreover, multi-object localization of US breast images can be considered in future employment.

Subject Areas

Automatic localization; human visual mechanism; superpixel contrast feature; ultrasound breast tumor.

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