A novel Graph-based Segmentation method for Breast Ultrasound Images

作者：Luo Yaozhong; Han Shaojuan; Huang Qinghua^*

来源：International Conference on Digital Image Computing - Techniques and Applications (DICTA), 2016-11-30 To 2016-12-02.

DOI：10.1109/DICTA.2016.7796992

• breast tumor
• ultrasound
• graph theory
• image segmentation
• multi-objective optimization

Breast cancer occurs to 8% women during their lifetime, and is a leading cause of death among women. Breast ultrasound (BUS) image segmentation which is the essential process for further analysis, is a very challenging task due to the poor image quality. In this paper, we propose a new segmentation scheme to combine both region-and edge-based information based on the robust graph-based (RGB) segmentation method and the particle swarm optimization (PSO) algorithm. The only interaction required is to select two diagonal points to determine a region of interest (ROI) on the original image. The ROI image is smoothed by a bilateral filter and then contrast-enhanced by histogram equalization. Then, the enhanced image is filtered by pyramid mean shift to improve homogeneity. With the optimization of the PSO algorithm, the RGB segmentation method is performed to segment the filtered image. To validate our method, experiments have been conducted on datasets. The segmentation results of our method have been compared with the corresponding results obtained by three existing approaches, and four metrics have been used to measure the segmentation performance. The experimental results show that our method can accurately segment BUS images.

• 出版日期2016
• 单位华南理工大学