为了更好地进行图像弹性点的配准，提出了一种利用Hausdorff距离测度的弹性点配准方法。该方法以B样条为弹性形变模型，并具有较强的抵御杂点影响的能力。在此基础上又提出了序贯更新策略，即通过将源图像和控制点网格进行分块的方法来序贯更新弹性配准参数，从而进一步提高了算法的运算速度。为验证该方法的配准效果，采用该方法进行了合成图像、手写字体和脑部MRI图像的弹性配准实验。实验结果表明，该方法在基于特征的弹性配准应用中具有较好的使用效果。

Elastic registration is a difficult problem in image registration technology. An elastic point registration method using the Hausdorff distance is presented in this paper. With B-splines as the deformation model, the proposed method is able to handle elastic deformations between the images to be registered. Since no correspondence is required to be established between two point sets, the method is robust to outliers. In addition, a sequential updating strategy is introduced to further speed up the elastic registration method by dividing the source image and the grid of control points into separate blocks. The performance of the proposed method is demonstrated and validated in three experiments, a synthetic image registration experiment, a hand-drawn character registration experiment, and a brain MRI image registration experiment. While these results are somewhat preliminary, they clearly demonstrate the applicability of our method to real world tasks involving feature-based elastic registration.