Learning shape statistics for hierarchical 3D medical image segmentation

Wuxia Zhang, Yuan Yuan, Xuelong Li, Pingkun Yan

科研成果: 书/报告/会议事项章节会议稿件同行评审

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.

源语言英语
主期刊名ICIP 2011
主期刊副标题2011 18th IEEE International Conference on Image Processing
2189-2192
页数4
DOI
出版状态已出版 - 2011
已对外发布
活动2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, 比利时
期限: 11 9月 201114 9月 2011

出版系列

姓名Proceedings - International Conference on Image Processing, ICIP
ISSN(印刷版)1522-4880

会议

会议2011 18th IEEE International Conference on Image Processing, ICIP 2011
国家/地区比利时
Brussels
时期11/09/1114/09/11

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