使用特征点与灰度值的医学图像局部配准方法

针对医学图像配准中,存在某些图像间大部分区域没有差异或者存在差异但不被关心的情况,提出了一种局部图像配准方法。该方法使用局部可控的紧支撑径向基函数作为配准变换函数,通过在感兴趣区域设置特征点,将变换函数作用范围限制在图像中某一特定区域,保持其他区域不发生变形。利用图像间的互信息量作为测度函数,更加精确地求解变换函数。在优化策略的选择中,将图像配准看作为寻优过程,采用基于小生境的遗传算法优化变换函数参数,能够克服经典遗传算法早熟、搜索能力差等缺点。通过对已知变换函数的仿真图像与真实医学图像进行实验,结果表明该算法能够准确地找到较优的变换函数,并且将作用区域限制在较小范围内。该方法结合了基于特征点和基于像素配准方法的优点,有效的搜索策略保证了变换函数准确性,是一种可行的、鲁棒的局部医学图像配准方法。

Local Registration of Medical Images Using Feature Point and Intensity Information

A local image registration method is proposed specially for the conditions when images, in medical image registration, are largely similar or the differences are not significant. The transformation function is a radial basis function with compact support and its locality can be conveniently controlled by distributing the feature points into the desired regions, which especially allows us to deal with local changes in medical images. Mutual information is chosen as cost function in order that the transformation function can be achieved accurately. In the process of the optimization, the image registration is treated as optimal problem and niche genetic algorithm is employed to optimize the transformation function parameters because it can overcome the drawbacks of premature and weak exploitation capabilities compared with genetic algorithm. The experiments on the simulated image with the known transformation function and the real image are conducted by using the proposed method. The results show that the optimal transformation function can be found and its action domain is controlled within a relatively small region. The presented method, which is a feasible and robust medical image registration approach, exploits the advantages of both feature points and intensity information and can obtain the accurate transformation function by the efficient optimization strategy.