Registration of medical images using an interpolated closest point transform: method and validation.
Registration of medical images using an interpolated closest point transform: method and validation.
复制标题
使用插值最近点变换注册医学图像：方法和验证。
DOI：
10.1016/j.media.2004.01.002
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发表时间：
2004
期刊：
影响因子：
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通讯作者：
Dawant,BenoitM
中科院分区：
文献类型：
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作者：
Cao,Zhujiang;Pan,Shiyan;Li,Rui;Balachandran,Ramya;Fitzpatrick,JMichael;Chapman,WilliamC;Dawant,BenoitM
图像配准是医学诊断的重要过程。自从范德比尔特大学 Fitzpatrick 领导的大型站点间回顾性验证研究以来，基于体素的方法，更具体地说，基于相互信息的配准方法（例如，参见 [IEEE Trans. Med. Imag. 22 (8) (2003) 986] 对这些方法的综述）已被视为解决刚体受试者内配准问题的首选方法。在本研究中，我们提出了一种基于迭代最近点算法和预先计算的最近点图的方法，该方法是对 Sethian 提出的快速行进方法稍加修改而获得的。预先计算最近点图可以加速该过程，因为在每个迭代点处都可以通过查表来建立对应关系。我们还表明，由于最近点图是在规则网格上定义的，因此会引入配准误差，我们提出了一种解决此问题的插值方案。该方法已在合成图像和真实图像上进行了测试，并使用回顾性配准评估项目提供的数据集对配准结果进行了定量评估。对于这些体积，使用水平集技术自动提取 MR 和 CT 头部表面。结果表明，在这些数据集上，这种配准方法产生的准确率与基于体素的方法获得的准确率相当。
Image registration is an important procedure for medical diagnosis. Since the large inter-site retrospective validation study led by Fitzpatrick at Vanderbilt University, voxel-based methods and more specifically mutual information-based registration methods (see for instance [IEEE Trans. Med. Imag. 22 (8) (2003) 986] for a review on these methods) have been regarded as the method of choice for rigid-body intra-subject registration problems. In this study we propose a method that is based on the Iterative Closest Point algorithm and a pre-computed closest point map obtained with a slight modification of the fast marching method proposed by Sethian. Pre-computing the closest point map speeds up the process because at each iteration point correspondence can be established by table lookup. We also show that because the closest point map is defined on a regular grid it introduces a registration error and we propose an interpolation scheme that addresses this issue. The method has been tested both on synthetic and real images, and registration results have been assessed quantitatively using the data set provided by the Retrospective Registration Evaluation Project. For these volumes, MR and CT head surfaces were extracted automatically using a level-set technique. Results show that on these data sets this registration method leads to accuracy numbers that are comparable to those obtained with voxel-based methods.