基于互信息与梯度相似性相结合的医学图像配准方法

Medical image registration based on mutual information combined with gradient similarity

针对传统互信息配准方法未利用图像空间信息的缺点,提出一种将互信息与梯度相似性结合的医学图像配准方法.待配准图像的每组对应点的梯度相似性包括方向相似性和模值相似性.待配准图像整体梯度相似性系数由各对应点对的梯度相似性之和决定,该系数与传统互信息的乘积作为图像配准的测度.利用2D多模图像分别进行平移、旋转、采样,得到配准函数曲线,并给出具体的配准实例.实验结果表明,该方法比传统互信息有更高的鲁棒性和精度.

To solve the drawback that typical mutual information-based registration neglects the spatial information of images, a new medical image registration method is developed by combining mutual information with gradient similarity. The gradient similarity of each pair of corresponding points includes direction similarity and module similarity. The summation of the similarity term for all sample pairs gives the gradient similarity of two registration images, which is multiplied by the mutual information to form the final registration metric. Registration functions are analyzed and compared, applying in different transform such as translation, rotation and sub-sampling of 2D multi-modal images, respectively. Experimental results show that the new method performs better than typical mutual information in robustness and precision.