| Optimized graph-based segmentation for ultrasound images | |
Huang, Qinghua1; Bai, Xiao2; Li, Yingguang1; Jin, Lianwen1; Li, Xuelong3
| |
| 作者部门 | 光学影像学习与分析中心 |
| 2014-04-10 | |
| 发表期刊 | NEUROCOMPUTING
 |
| ISSN | 0925-2312 |
| 卷号 | 129期号:SI页码:216-224 |
| 摘要 | Segmentation of medical images is an inevitable image processing step for computer-aided diagnosis. Due to complex acoustic inferences and artifacts, accurate extraction of breast lesions in ultrasound images remains a challenge. Although there have been many segmentation techniques proposed, the performance often varies with different image data, leading to poor adaptability in real applications. Intelligent computing techniques for adaptively learning the boundaries of image objects are preferred. This paper focuses on optimization of a previously documented method called robust graph-based (RGB) segmentation algorithm to extract breast tumors in ultrasound images more adaptively and accurately. A novel technique named as parameter-automatically optimized robust graph-based (PAORGB) image segmentation method is accordingly proposed and performed on breast ultrasound images. A particle swarm optimization algorithm is incorporated with the RGB method to achieve optimal or approximately optimal parameters. Experimental results have shown that the proposed technique can more accurately segment lesions from ultrasound images compared to the RGB and two conventional region-based methods. (C) 2013 Elsevier By. All rights reserved. |
| 文章类型 | Article |
| 关键词 | Evolutionary Learning Ultrasound Image Segmentation Particle Swarm Optimization Graph Theory |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1016/j.neucom.2013.09.038 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | BREAST-TUMORS ; LEVEL SET ; FEATURES |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence |
| WOS记录号 | WOS:000332132400026 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/22401 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.S China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Guangdong, Peoples R China 2.Beihang Univ, Sch Comp Sci & Engn, Beijing 100191, Peoples R China 3.Chinese Acad Sci, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Qinghua,Bai, Xiao,Li, Yingguang,et al. Optimized graph-based segmentation for ultrasound images[J]. NEUROCOMPUTING,2014,129(SI):216-224. |
| APA | Huang, Qinghua,Bai, Xiao,Li, Yingguang,Jin, Lianwen,&Li, Xuelong.(2014).Optimized graph-based segmentation for ultrasound images.NEUROCOMPUTING,129(SI),216-224. |
| MLA | Huang, Qinghua,et al."Optimized graph-based segmentation for ultrasound images".NEUROCOMPUTING 129.SI(2014):216-224. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Optimized graph-base（5860KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY | 请求全文 | |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
修改评论