特征点对引导的医学图像弹性配准方法

采用最优化方法进行弹性图像配准时,常常会因为初值选择不合适导致优化过程收敛到局部极值,从而使配准失败。针对该问题,提出了利用一对匹配的特征点来引导迭代配准过程的算法。首先,根据一对匹配的特征点确定图像初始局部配准区域;然后,在算法迭代过程中逐渐扩展局部配准区域直至覆盖整个图像。建立初始局部配准区域及局部配准区域扩展时,根据特征点对的空间位置关系,以及医学图像的形变场在统计特性上是高斯马尔可夫随机场的先验知识,来估计局部配准区域形变参数向量的初值;在对局部配准区域进行配准时,基于图像像素灰度统计信息的配准方法被用来求解局部配准区域的形变场参数向量。实验证明,该算法能够有效克服弹性配准算法容易陷入局部极值的问题。

Medical Image Non-Rigid Registration Method Guided by a Matched Feature Point Pair

When using optimization techniques to solve non-rigid image registration, improper initial values often cause the optimization process to converge to a local minima and lead to a failed image registration. In order to solve this problem, we propose a new non-rigid image registration procedure which is guided by a pair of matched feature points. Firstly, an initial local region is determined according to the pair of matched feature points. Secondly, the local registration region expands gradually to cover the whole image as the optimization process continues. In the process that the initial local registration region is determined and expanded, the values of the deformation parameter vector are estimated according to the spatial locations of the matched feature point pair and the statistical prior that the deformation field is a Gaussian Markov random field (GMRF). The parameter vector of the deformation field in the local registration region is solved by using the registration method based on the image's intensity statistical information. Experimental results show that the proposed approach can effectively overcome the problem that the non-rigid registration method is liable to be trapped in a local minimum.