| Group sparse reconstruction for image segmentation |
| Lu, Xiaoqiang ; Li, Xuelong
|
| 作者部门 | 光学影像学习与分析中心
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| 2014-07-20
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| 发表期刊 | NEUROCOMPUTING
 |
| ISSN | 0925-2312
|
| 卷号 | 136页码:41-48 |
| 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. |
| 文章类型 | Article
|
| 关键词 | Prostate Segmentation
One-class Classifier
Active Shape Model
Group Lasso
|
| WOS标题词 | Science & Technology
; Technology
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| DOI | 10.1016/j.neucom.2014.01.034
|
| 收录类别 | SCI
; EI
|
| 关键词[WOS] | LEVEL SET METHOD
; AUTOMATIC SEGMENTATION
; SHAPE
; CLASSIFICATION
; PROSTATE
; MODEL
|
| 语种 | 英语
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| WOS研究方向 | Computer Science
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| WOS类目 | Computer Science, Artificial Intelligence
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| WOS记录号 | WOS:000335708800005
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| 引用统计 |
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| 文献类型 | 期刊论文
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| 条目标识符 | http://ir.opt.ac.cn/handle/181661/22394
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| 专题 | 光谱成像技术研究室
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| 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
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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.
|
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