基于多尺度Harris角点SAM的医学图像配准算法

为满足医学图像配准对多分辨率，高配准率，低时耗率的高要求，提出了一种新颖的基于多尺度Harris角点方根-算术均值距离（SAM）的配准算法。该算法通过对图像进行小波多尺度积边缘检测和多尺度Harris角点检测，首先得到了估计变换参数；然后利用角点间的SAM作为相似性测度函数来获得最佳匹配点对，并通过最小二乘得到最终配准参数。实验表明，算法可实现含噪声图像以及不同分辨率的多模医学图像的配准，由于算法只对角点匹配，无须最优搜索，从而不仅大大减少了计算量，而且避免了陷入局部极值的情况。最后，通过3类实验验证了算法的可行性和鲁棒性。

Medical image registration algorithm based on the SAM of multi-scale Harris corner

Registration of multi-resolution, multi-source images is a difficult task. This paper proposes a novel registration algorithm based on SAM (square root arithmetic mean divergence) information of multi-scale Harris corners (CSAM for short). In this algorithm, the estimated transform parameters are obtained by extracting a multi-scale contour and detecting multi-scale Harris corner. Then CSAM is used as similarity measure function, and several optimized match points are obtained. The final registration parameters can be acquired by using least squares method. Registration of medical images with noise and multi-resolutions can be realized by this algorithm, calculation time is reduced and local extremum can be avoided due to reduce matching corner points which need not optimal search. Finally, experimental results show that this algorithm has the advantages such as high precision, high speed and good robust.