Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2017, Article ID 9157341, 18 pages
Research Article

A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images

1School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
2Department of Ultrasound, The Cancer Center of Sun Yat-sen University, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong, China
3College of Information Engineering, Shenzhen University, Shenzhen 518060, China
4Center for OPTical IMagery Analysis and Learning (OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shaanxi 710119, China

Correspondence should be addressed to Qinghua Huang; nc.ude.tucs@gnauhhq

Received 9 September 2016; Revised 21 January 2017; Accepted 14 March 2017; Published 27 April 2017

Academic Editor: Cristiana Corsi

Copyright © 2017 Yaozhong Luo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a 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 into the robust graph-based (RGB) segmentation method. 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 particle swarm optimization (PSO) algorithm, the RGB segmentation method is performed to segment the filtered image. 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 the method achieves the best overall performance and gets the lowest ARE (10.77%), the second highest TPVF (85.34%), and the second lowest FPVF (4.48%).