双向梯度归一化互信息医学图像配准方法

传统互信息配准方法未利用图像的空间信息,为此,提出一种将互信息与梯度相似性相结合的双向医学图像配准方法.首先以图像A为参考,求图像A和待配准图像B的每组对应点的梯度相似性,并在计算相似性之前引入高斯算子以降低噪声影响,将梯度相似性因子与归一化互信息的乘积作为图像配准的正向测度;反过来,再以图像B为参考计算逆向的梯度归一化互信息.由此得到双向梯度归一化互信息.实验结果表明,该方法比传统归一化互信息和梯度归一化互信息方法有更高的鲁棒性和精度.

Bilateral Medical Image Registration Based on Normalized Mutual Information Combined with Gradient Information

Combining mutual information of an image with its gradient information,a new bilateral image registration method is developed to use the image’s spatial information conventional registration methods neglect.Taking an image A as a reference,the gradient similarities are calculated between the corresponding points of image A and B to be registered after a Gaussian operator is introduced to reduce the effect of noises,and the normalized mutual information multiplied by the gradient similarity is named "forward measure".Conversely,using the image B as a reference,the gradient similarity is thus calculated,thereby "backward measure" being determined.Then,the normalized mutual information combined with bilateral gradient similarity is obtained.Experimental results show that the new method is more robust and accurate than the conventional ones.