Cao Zhujiang, Pan Shiyan, Li Rui, Balachandran Ramya, Fitzpatrick J Michael, Chapman William C, Dawant Benoit M
Department of Biomedical Engineering, Box 1662, Station B, Vanderbilt University, Nashville, TN 37235, USA.
Med Image Anal. 2004 Dec;8(4):421-7. doi: 10.1016/j.media.2004.01.002.
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.
图像配准是医学诊断中的一个重要步骤。自从范德堡大学的菲茨帕特里克领导的大型多中心回顾性验证研究以来，基于体素的方法，更具体地说是基于互信息的配准方法（例如，有关这些方法的综述，请参见[《IEEE医学成像汇刊》22(8)(2003)986]），已被视为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通过对塞西安提出的快速行进方法稍加修改而获得的预计算最近点图的方法。预计算最近点图加快了处理速度，因为在每次迭代中，可以通过查表来建立点对应关系。我们还表明，由于最近点图是在规则网格上定义的，它会引入配准误差，并且我们提出了一种插值方案来解决这个问题。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。对于这些体积数据，使用水平集技术自动提取了MR和CT头部表面。结果表明，在这些数据集上，这种配准方法得到的精度数值与基于体素的方法相当。
