Abstract: In order to reduce local maximum and misregistration of mutual information in medical image registration，three information measures based on generalized entropy instead of the Shannon entropy，named as FRI-alpha，SRI-alpha and GMI-t information measures，are proposed.The convergence width and radius are used for evaluating the measure convergence.The computing time，convergence and accuracy are studied by applying these measures to rigid registration of Computed Tomography（CT）/Magnetic Resonance（MR） and MR-T1/T2 simulated images.The results of tests show that the generalized entropy measures outperform normalized mutual information in convergence performance，without compromising computational speed and registration accuracy.

摘要： 针对互信息测度在配准医学图像时易陷入局部极值的缺点，将Shannon熵扩展到广义熵，提出了三种基于广义熵的信息测度。对于收敛性能的评价，提出收敛宽度和收敛半径的概念。通过人体脑部CT/MR和MR-T1/T2图像的刚体配准实验，从计算时间、收敛性能和配准精度方面，对归一化互信息、广义熵信息测度进行了比较与分析。实验结果表明，在不损失计算时间和配准精度的前提下，广义信息熵测度SRI_0.9和GMI_0.9的收敛性能优于归一化互信息测度，对噪声有很强的鲁棒性。