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中文摘要：
基于像素的图像配准测度函数通常采用归一化互信息，其具有良好的配准性能，能够达到亚像素配准，但对于多模态图像配准，由于局部极值的影响以及全局最大值的捕获范围较窄，容易陷入局部极值导致配准失败。结构相似度通常用来评估图像质量，可以反映图像间视觉效果和结构信息的差别，同时与像素灰度的统计分布相关，当空间位置发生改变时，图像问的结构相似度也随之发生变化。对其进行适当修改，作为一种新的测度函数运用于图像配准。实验结果表明：这种修改后的结构相似度作为测度函数，其配准曲线为良好的上凸函数，没有明显的局部极值，图像匹配时对应其全局最大值，并且捕获范围较宽，鲁棒性较高，但运算速度较慢，对强噪声比较敏感；应用于三维图像配准，即使是10个参数的仿射变换，也能够达到亚像素级配准精度。
英文摘要：
The similarity metrics of voxel-based image registration are usually Normalized Mutual Information （NMI）, and its good registration properties can make images to achieve sub-voxel registration. However, the local extrema and the narrow capture range of global maximum for multi-modal images are easy to cause registration to fail. The structural similarity function has been used to assess image quality. It may reflect the difference of the visual effect and structural information between images, and is associated with the statistical distribution of the voxel gray. When the spatial location between images is changed, the structural similarity also will be changed. We modify this function to make it available for image registration. Simulation results demonstrate that the registration curves of this modified structural similarity （MSSIM） used as a new registration metric show a good convex upward function, and have no significant local extrema. Moreover, the global maximum is located exactly. Especially, the capture range of the global maximum is wide, and hence its robustness is strong. In addition, it is sensitive to strong noise and its operation speed is slow. The metric MSSIM can achieve sub-pixel registration accuracy for three-dimensional image registration even if a 10-parameter affine transformation.
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