| A Variational Approach to Simultaneous Image Segmentation and Bias Correction | |
Zhang, Kaihua1; Liu, Qingshan1; Song, Huihui1; Li, Xuelong2
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| 2015-08-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS
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| 卷号 | 45期号:8页码:1426-1437 |
| 摘要 | This paper presents a novel variational approach for simultaneous estimation of bias field and segmentation of images with intensity inhomogeneity. We model intensity of inhomogeneous objects to be Gaussian distributed with different means and variances, and then introduce a sliding window to map the original image intensity onto another domain, where the intensity distribution of each object is still Gaussian but can be better separated. The means of the Gaussian distributions in the transformed domain can be adaptively estimated by multiplying the bias field with a piecewise constant signal within the sliding window. A maximum likelihood energy functional is then defined on each local region, which combines the bias field, the membership function of the object region, and the constant approximating the true signal from its corresponding object. The energy functional is then extended to the whole image domain by the Bayesian learning approach. An efficient iterative algorithm is proposed for energy minimization, via which the image segmentation and bias field correction are simultaneously achieved. Furthermore, the smoothness of the obtained optimal bias field is ensured by the normalized convolutions without extra cost. Experiments on real images demonstrated the superiority of the proposed algorithm to other state-of-the-art representative methods. |
| 文章类型 | Article |
| 关键词 | Bias Field Computer Vision Energy Minimization Image Segmentation Variational Approach |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TCYB.2014.2352343 |
| 收录类别 | SCI ; EI |
| 关键词[WOS] | LEVEL SET METHOD ; ACTIVE CONTOURS DRIVEN ; MR-IMAGES ; INTENSITY INHOMOGENEITIES ; FIELD ESTIMATION ; REGION COMPETITION ; FITTING ENERGY ; MINIMIZATION ; NONUNIFORMITY ; INFORMATION |
| 语种 | 英语 |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS记录号 | WOS:000358213100004 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/25257 |
| 专题 | 光谱成像技术研究室 |
| 作者单位 | 1.Nanjing Univ Informat Sci & Technol, Smart Grp, Nanjing, Jiangsu, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, State Key Lab Transient Opt & Photon, Ctr OPT IMagery Anal & Learning, Xian 710119, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Kaihua,Liu, Qingshan,Song, Huihui,et al. A Variational Approach to Simultaneous Image Segmentation and Bias Correction[J]. IEEE TRANSACTIONS ON CYBERNETICS,2015,45(8):1426-1437. |
| APA | Zhang, Kaihua,Liu, Qingshan,Song, Huihui,&Li, Xuelong.(2015).A Variational Approach to Simultaneous Image Segmentation and Bias Correction.IEEE TRANSACTIONS ON CYBERNETICS,45(8),1426-1437. |
| MLA | Zhang, Kaihua,et al."A Variational Approach to Simultaneous Image Segmentation and Bias Correction".IEEE TRANSACTIONS ON CYBERNETICS 45.8(2015):1426-1437. |
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| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| A Variational Approa（2027KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
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