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

• Published in: BioMed research international Vol. 2017; no. 2017; pp. 1 - 18
• Main Authors: Li, Xuelong, Huang, Qing-Hua, Liu, Longzhong, Luo, Yaozhong
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2017; Hindawi; John Wiley & Sons, Inc
• Subjects: Algorithms; Collaboration; Computer-aided medical diagnosis; Contrast Media - therapeutic use; Humans; Image Enhancement - methods; Image Processing, Computer-Assisted - methods; Image segmentation; Laboratories; Medical imaging; Methods; NMR; Nuclear magnetic resonance; Science; Tumors; Ultrasonic imaging; Ultrasonography - methods; Ultrasound imaging; Watersheds; United States > US
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2017/9157341

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%).