Geostatistical Medical Image Registration

机译：地统计学医学图像配准

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We propose a novel approach to landmark-based medical image registration based on the geostatical method of Kriging prediction. Our method exploits the spatial statistical relation between two images, as estimated using general-purpose registration algorithms, in order to construct an optimum predictor of the displacement field. High accuracy is achieved by using an estimated spatial model of the displacement field directly from the image data, while practically circumventing the difficulties that prevented Kriging from being widely used in image registration.

机译：我们提出了一种基于克里格预测的静态方法的基于地标的医学图像配准的新方法。我们的方法利用使用通用配准算法估算的两幅图像之间的空间统计关系，以构造位移场的最佳预测因子。通过直接从图像数据中使用位移场的估计空间模型来实现高精度，同时实际上避免了阻碍Kriging广泛用于图像配准的困难。

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《6th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2003) Pt.II; Nov 15-18, 2003; Montreal, Canada》|2003年|p.894-901|共8页
会议地点 Montreal(CA);Montreal(CA)
作者
J. Ruiz-Alzola; E. Suarez; C. Alberola-Lopez; S.K. Warfield; C.-F. Westin;
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Medical Technology Center Universidad de Las Palmas de Gran Canada, Spain;

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原文格式 PDF
正文语种 eng
中图分类 Q-;
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3. Supervised quality assessment of medical image registration: Application to intra-patient CT lung registration [J] . MuenzingS.E.A., vanGinnekenB., MurphyK., Medical image analysis . 2012,第8期

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5. Medical Image Registration by Neural Networks A Regression-Based Registration Approach [D] . Gouveia, Ana Isabel Antunes Dias Rodrigues. 2014

机译：神经网络的医学图像配准-基于回归的配准方法
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7. Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration [O] . J. M. Sloan, K. A. Goatman, J. P. Siebert 2018

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Geostatistical Medical Image Registration

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