针对单一特征引导图像配准的准确度有限性，提出了一种同时使用轮廓与特征点的医学图像弹性配准方法。半自动的特征点提取方法既可以保证提取的精确性又能够避免繁琐的特征点对应关系建立过程。对于提取的轮廓，在保证外形的基础之上，通过轮廓直线化操作减少提取轮廓中关键点的数量，以提高计算效率。以两幅待配准图像中的特征点对间距离与轮廓对间距离累加和作为图像配准测度函数，选择ICP算法框架迭代地求解最优配准变换函数。通过与其他测度函数进行比较和真实图像实验结果对比，其结果表明，该算法由于采用轮廓与特征点同时引导图像配准，其配准效果好于单独使用特征点或者轮廓的图像配准算法。该算法既能匹配图像的整体结构信息（轮廓）又能对齐图像中感兴趣的生理解剖位置（特征点），更加准确地反映图像间差异情况，是一种快速、精确的医学图像配准方法。

Due to the limitation of medical image registration introduced by one kind of feature, a medical image elastic registration method using contour and feature points is proposed. Feature points can be extracted with semi automatic method, which can not only ensure the accuracy of the extraction but also avoid the complicated process of building corresponding relationship of feature points. On the basis of keeping shape of the contour, contour linearization is employed to reduce key points in the extracted contour, which improves the computation efficiency. The sum of the distances between feature points and the distances between contours is chosen as the criterion of image registration. The registration transformation is resolved iteratively by the frame of ICP algorithm. The analysis of the choice of cost function and the experimental comparison with other methods using real images demonstrate that the results of the proposed algorithm are better than those of other methods that only use the point or the contour. The presented method can not only register the structure of images (contour) but also align the interested anatomic positions (feature points), which represents the exact difference between images, and is a fast and accurate medical image registration algorithm.