Learning shape statistics for hierarchical 3D medical image segmentation
文献类型：会议论文
| 作者 | ZhangWuxia ; YuanYuan ; LiXuelong ; YanPingkun |
| 出版日期 | 2011 |
| 会议名称 | 2011 18th ieee international conference on image processing, icip 2011 |
| 会议日期 | september 11, 2011 - september 14, 2014 |
| 会议地点 | brussels, belgium |
| 关键词 | 3D image segmentation shape modeling manifold learning surface patch shape statistics |
| 页码 | 2189-2192 |
| 通讯作者 | zhang wuxia |
| 英文摘要 | accurate image segmentation is important for many medical imaging applications, whereas it remains challenging due to the complexity in medical images, such as the complex shapes and varied neighbor structures. this paper proposes a new hierarchical 3d image segmentation method based on patient-specific shape prior and surface patch shape statistics (surpass) model. in the segmentation process, a coarse-to-fine, two-stage strategy is designed, which contains global segmentation and local segmentation. in the global segmentation stage, patient-specific shape prior is estimated by using manifold learning techniques to achieve the overall segmentation. in the second stage, surpass is computed to solve the problem of poor segmentation at certain surface patches. the effectiveness of the proposed 3d image segmentation method has been demonstrated by the experiments on segmenting the prostate from a series of mr images. |
| 收录类别 | EI ; CPCI |
| 产权排序 | 1 |
| 会议主办者 | ieee; ieee signal processing society |
| 会议录 | proceedings - international conference on image processing, icip
 |
| 会议录出版者 | ieee computer society |
| 会议录出版地 | 445 hoes lane - p.o.box 1331, piscataway, nj 08855-1331, united states |
| 语种 | 英语 |
| ISSN号 | 1522-4880 |
| 源URL | [http://ir.opt.ac.cn/handle/181661/20152]  |
| 专题 | 西安光学精密机械研究所_研究生部 |
| 推荐引用方式 GB/T 7714 | ZhangWuxia,YuanYuan,LiXuelong,et al. Learning shape statistics for hierarchical 3D medical image segmentation[C]. 见:2011 18th ieee international conference on image processing, icip 2011. brussels, belgium. september 11, 2011 - september 14, 2014. |
入库方式： OAI收割
来源：西安光学精密机械研究所
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