Group sparse reconstruction for image segmentation

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

	Group sparse reconstruction for image segmentation
	Lu, Xiaoqiang; Li, Xuelong
刊名	neurocomputing
	2014-07-20
卷号	136 页码:41-48
关键词	Prostate segmentation One-class classifier Active shape model Group lasso
ISSN号	0925-2312
英文摘要	image segmentation is a fundamental problem in computer vision and image analysis. specially, the segmentation of medical images can assist doctors in making decisions. due to the lack of distinctive features to describe the boundary of an organ and match function with high performance for features, medical image segmentation is difficult to be achieved with high accuracy. in this paper, an one-class classifier is proposed as the match function to decide whether the pixel belongs to the boundary or not. the proposed method is comprised of two steps. at first, a feature vector space is built with the gradient feature and its statistical information in the training stage. in the test image, a feature vector of one candidate probably being located on the boundary is reconstructed by sparse coding with the feature vector space. after reconstruction, the candidate is classified belonging to boundary or non-boundary via the reconstruction based one-class classifier. then, in order to maintain the consistency between the candidates which are neighbors to each other, the neighboring candidates are coded using group lasso with the same dictionary. compared to the traditional methods, the proposed one has three advantages. firstly, it solves the non-gaussian distribution problem of the positive samples. secondly, it avoids large variation among the negative samples. thirdly, the relationship of the neighboring candidates is considered and used in classification, which is ignored in other methods. the proposed method is validated on 52 mr images of prostate and outperforms mahalanobis distance, which is considered as one of the state-of-the-art match functions. the experimental results show that the segmentation accuracy can be significantly improved by the proposed method with one-class classification. (c) 2014 elsevier b.v. 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]	level set method ; automatic segmentation ; shape ; classification ; prostate ; model
收录类别	SCI ; EI
语种	英语
WOS记录号	WOS:000335708800005
公开日期	2015-03-18
内容类型	期刊论文
源URL	[http://ir.opt.ac.cn/handle/181661/22394]
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	Chinese Acad Sci, Xian Inst Opt & Precis Mech, Ctr OPT IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian 710119, Shaanxi, Peoples R China
推荐引用方式 GB/T 7714	Lu, Xiaoqiang,Li, Xuelong. Group sparse reconstruction for image segmentation[J]. neurocomputing,2014,136:41-48.
APA	Lu, Xiaoqiang,&Li, Xuelong.(2014).Group sparse reconstruction for image segmentation.neurocomputing,136,41-48.
MLA	Lu, Xiaoqiang,et al."Group sparse reconstruction for image segmentation".neurocomputing 136(2014):41-48.