基于最小生成树的DoG关键点医学图像配准

针对医学图像配准对鲁棒性强、准确性高和速度快的要求，提出一种基于最小生成树的DoG(difference of Gaussian)关键点配准算法。该算法首先从图像上提取DoG关键点，然后将关键点对应的灰度信息融入联合Rényi熵中，最后使用最小生成树来估计联合Rényi熵。新算法结合了DoG关键点的鲁棒性和最小生成树估计Rényi熵的高效性。实验结果表明，在图像含有噪声、灰度不均匀和初始变换范围较大的情况下，该算法在达到良好配准精度的同时，具有较强的鲁棒性和较快的速度。

DoG keypoints medical image registration based on minimum spanning tree

For medical image registration of good robustness, high-accuracy and speed requirements, this paper proposes a DoG(difference of Gaussian) keypoints image registration algorithm based on Rényi entropy. This algorithm extracts DoG key points from images, then incorporates grey scale information of the key point into the joint Rényi entropy, and estimates joint Rényi entropy directly using minimum spanning tree. The new algorithm combines the robustness of DoG key points and the high speed of Rényi entropy estimated by the minimum spanning tree. Experimental results show that in the images with noise, non-uniform intensity and large scope of the initial misalignment case, the algorithm achieves better robustness and higher speed while maintaining good registration accuracy.