考虑噪声干扰的医学图像点对配准算法及其误差预测

提出了考虑噪声干扰的医学图像点对配准问题的最大似然估计算法以及目标配准误差（TRE）的预测方法.对定义在Mahalanobis距离空间的绝对定向问题（AOP）进行迭代求解，使其结果收敛于最大似然解；应用误差传递理论计算配准结果的方差，通过方差传递公式计算TRE的数学期望，并用于评价配准结果和优化基准点的空间配置.数值模拟实验结果表明，与AOP的封闭解法相比，所提出算法的估计结果的精度较高，其对TRE预测的相对误差小于2%，对配准结果标准差预测的相对误差小于4%.

Point-Based Medical Image Registration and Error Prediction Considering Noise Perturbation

A novel point-based registration method for medical use considering noise perturbation was presented. The method is based on maximum likelihood esitimation (MLE), which is achieved by iteratively minimizing the absolute orientation problem (AOP) defined in the Mahalanobis space. The method can estimate not only the rigid transformation but also its variance based on the error propagation theory. The variance of the rigid transform is further propagated into the target registration error (TRE) and the expectation of the TRE is calculated for registration evaluation and fiducial marker (FM) configuration optimization. The numerical simulation shows the proposed method outperforms the classic AOP solution in both correctness and precision with the relative predictive error of the TRE less than 2%, of the standard deviation of the estimated transformation less than 4%.