Optimized graph-based segmentation for ultrasound images

CORC > 西安光学精密机械研究所 > 中国科学院西安光学精密机械研究所 > 光学影像学习与分析中心

	Optimized graph-based segmentation for ultrasound images
	Huang, Qinghua 1; Bai, Xiao 2; Li, Yingguang 1; Jin, Lianwen 1; Li, Xuelong3
刊名	neurocomputing
	2014-04-10
卷号	129 期号:si 页码:216-224
关键词	Evolutionary learning Ultrasound image segmentation Particle swarm optimization Graph theory
ISSN号	0925-2312
英文摘要	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.
WOS标题词	science & technology ; technology
类目[WOS]	computer science, artificial intelligence
研究领域[WOS]	computer science
关键词[WOS]	breast-tumors ; level set ; features
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000332132400026
公开日期	2015-03-18
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/22401]
专题	西安光学精密机械研究所_光学影像学习与分析中心
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.