• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

传递逆一致流形配准

Transitive inverse-consistent manifold registration.

作者信息

Geng Xiujuan, Kumar Dinesh, Christensen Gary E

机构信息

Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.

出版信息

Inf Process Med Imaging. 2005;19:468-79. doi: 10.1007/11505730_39.

DOI:10.1007/11505730_39
PMID:17354718
Abstract

This paper presents a new registration method called Transitive Inverse-Consistent Manifold Registration (TICMR). The TICMR method jointly estimates correspondence maps between groups of three manifolds embedded in a higher dimensional image space while minimizing inverse consistency and transitivity errors. Registering three manifolds at once provides a means for minimizing the transitivity error which is not possible when registering only two manifolds. TICMR is an iterative method that uses the closest point projection operator to define correspondences between manifolds as they are non-rigidly registered. Examples of the TICMR method are presented for matching groups of three contours and groups of three surfaces. The contour registration is regularized by minimizing the change in bending energy of the curves while the surface registration is regularized by minimizing the change in elastic energy of the surfaces. The notions of inverse consistency error (ICE) and transitivity error (TE) are extended from volume registration to manifold registration by using a closest point projection operator. For the experiments in this paper, the TICMR method reduces the average ICE by 200 times (contour)/ 6 times (surface) and the average TE by 40 times (contour)/ 2-4 times (surface) compared to registering with a curvature constraint alone. Furthermore, the TICMR is shown to avoid some local minimum that are not avoided when registering with a curvature constraint alone.

摘要

本文提出了一种名为传递逆一致流形配准(TICMR)的新配准方法。TICMR方法在最小化逆一致性和传递性误差的同时,联合估计嵌入在高维图像空间中的三组流形之间的对应映射。一次性配准三组流形提供了一种最小化传递性误差的方法,而仅配准两组流形时这是不可能的。TICMR是一种迭代方法,在对流形进行非刚性配准时,使用最近点投影算子来定义流形之间的对应关系。给出了TICMR方法用于匹配三组轮廓和三组曲面的示例。轮廓配准通过最小化曲线弯曲能量的变化进行正则化,而曲面配准通过最小化曲面弹性能量的变化进行正则化。通过使用最近点投影算子,将逆一致性误差(ICE)和传递性误差(TE)的概念从体积配准扩展到流形配准。对于本文中的实验,与仅使用曲率约束进行配准相比,TICMR方法将平均ICE降低了200倍(轮廓)/6倍(曲面),将平均TE降低了40倍(轮廓)/2 - 4倍(曲面)。此外,TICMR被证明可以避免一些仅使用曲率约束进行配准时无法避免的局部最小值。

相似文献

1
Transitive inverse-consistent manifold registration.传递逆一致流形配准
Inf Process Med Imaging. 2005;19:468-79. doi: 10.1007/11505730_39.
2
Ray-tracing based registration for HRCT images of the lungs.基于光线追踪的肺部高分辨率CT图像配准
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):670-7.
3
Iterative most likely oriented point registration.迭代最可能定向点配准
Med Image Comput Comput Assist Interv. 2014;17(Pt 1):178-85. doi: 10.1007/978-3-319-10404-1_23.
4
On the dosimetric effect and reduction of inverse consistency and transitivity errors in deformable image registration for dose accumulation.在剂量累加的形变图像配准中,针对逆一致性和传递性误差的剂量学效应及降低方法。
Med Phys. 2012 Jan;39(1):272-80. doi: 10.1118/1.3666948.
5
Evaluation of 4D-CT lung registration.4D-CT肺配准的评估。
Med Image Comput Comput Assist Interv. 2009;12(Pt 1):747-54. doi: 10.1007/978-3-642-04268-3_92.
6
Landmark correspondence optimization for coupled surfaces.耦合曲面的地标对应优化
Med Image Comput Comput Assist Interv. 2007;10(Pt 2):818-25. doi: 10.1007/978-3-540-75759-7_99.
7
2D/3D registration of multiple bones.多块骨骼的二维/三维配准
Annu Int Conf IEEE Eng Med Biol Soc. 2007;2007:860-3. doi: 10.1109/IEMBS.2007.4352426.
8
Smoothing lung segmentation surfaces in three-dimensional X-ray CT images using anatomic guidance.利用解剖学引导在三维X射线CT图像中平滑肺部分割表面
Acad Radiol. 2005 Dec;12(12):1502-11. doi: 10.1016/j.acra.2005.08.008.
9
Pulmonary nodule registration in serial CT scans based on rib anatomy and nodule template matching.基于肋骨解剖结构和结节模板匹配的连续CT扫描中的肺结节配准
Med Phys. 2007 Apr;34(4):1336-47. doi: 10.1118/1.2712575.
10
3D/2D image registration: the impact of X-ray views and their number.3D/2D图像配准:X射线视图及其数量的影响
Med Image Comput Comput Assist Interv. 2007;10(Pt 1):450-7.

引用本文的文献

1
Consistency-based rectification of nonrigid registrations.基于一致性的非刚性配准校正
J Med Imaging (Bellingham). 2015 Jan;2(1):014005. doi: 10.1117/1.JMI.2.1.014005. Epub 2015 Mar 25.
2
Validation of a nonrigid registration error detection algorithm using clinical MRI brain data.使用临床脑部磁共振成像(MRI)数据对一种非刚性配准误差检测算法进行验证。
IEEE Trans Med Imaging. 2015 Jan;34(1):86-96. doi: 10.1109/TMI.2014.2344911. Epub 2014 Jul 30.
3
Comparative evaluation of registration algorithms in different brain databases with varying difficulty: results and insights.
不同难度的不同脑数据库中配准算法的比较评估:结果与见解
IEEE Trans Med Imaging. 2014 Oct;33(10):2039-65. doi: 10.1109/TMI.2014.2330355. Epub 2014 Jun 13.
4
Improving Intensity-Based Lung CT Registration Accuracy Utilizing Vascular Information.利用血管信息提高基于强度的肺部CT配准精度
Int J Biomed Imaging. 2012;2012:285136. doi: 10.1155/2012/285136. Epub 2012 Nov 28.
5
Accurate measurement of brain changes in longitudinal MRI scans using tensor-based morphometry.使用基于张量的形态测量学对纵向 MRI 扫描中的大脑变化进行精确测量。
Neuroimage. 2011 Jul 1;57(1):5-14. doi: 10.1016/j.neuroimage.2011.01.079. Epub 2011 Feb 23.
6
Non-Rigid Multi-Modal Image Registration Using Cross-Cumulative Residual Entropy.基于交叉累积剩余熵的非刚性多模态图像配准
Int J Comput Vis. 2007 Aug 1;74(2):201-215. doi: 10.1007/s11263-006-0011-2.
7
Learning task-optimal registration cost functions for localizing cytoarchitecture and function in the cerebral cortex.学习用于定位大脑皮层细胞结构和功能的任务最优配准代价函数。
IEEE Trans Med Imaging. 2010 Jul;29(7):1424-41. doi: 10.1109/TMI.2010.2049497. Epub 2010 Jun 7.
8
Spherical demons: fast diffeomorphic landmark-free surface registration.球形恶魔:快速的非刚性地标自由表面配准。
IEEE Trans Med Imaging. 2010 Mar;29(3):650-68. doi: 10.1109/TMI.2009.2030797. Epub 2009 Aug 25.
9
Morphological appearance manifolds in computational anatomy: groupwise registration and morphological analysis.计算解剖学中的形态外观流形:群组配准与形态分析。
Neuroimage. 2009 Mar;45(1 Suppl):S73-85. doi: 10.1016/j.neuroimage.2008.10.048. Epub 2008 Nov 12.
10
Spherical demons: fast surface registration.球形恶魔:快速表面配准
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):745-53. doi: 10.1007/978-3-540-85988-8_89.