Purpose: Mutual information (MI) has been a preferred choice of similarity measure for multi-modal image registration, but the accuracy and robustness of MI are not satisfactory as MI only considers the global intensity correlation while ignoring local and structural information. To address this problem, we combine MI with local and structural information.

Method: We bring structural information extracted by a modified Accelerated Segment Test (FAST) algorithm into MI. Traditional FAST is transferred into 3 D for the first time, and the 3 D-FAST based structural information is added into MI as another channel, thereby incorporating spatial and geometric information with intensity information in the registration.

Result: The robustness and accuracy of the proposed method were demonstrated in three experiments. The average registration errors of our method were 1.17, 1.33 and 1.20 compared to 1.47, 1.63 and 1.40 of LMI in T1-T2, T1-PD and T2-PD registration respectively.

Discussion: In this paper, we use the structural similarity computed by 3 D-FAST as the conditional information to encode spatial and geometric cues into LMI. In all of these three experiments, our method shows to be more robust and accurate than common registration methods based on information theory.

目的：互信息（MI）是用于多模式图像配准的相似性度量的首选，但是由于MI仅考虑全局强度相关性而忽略了局部和结构信息，因此MI的准确性和鲁棒性并不令人满意。为了解决这个问题，我们将MI与本地和结构信息相结合。

方法：我们将通过改进的加速分段测试（FAST）算法提取的结构信息引入MI。首次将传统FAST转换为3D，并将基于3D-FAST的结构信息作为另一个通道添加到MI中，从而将空间和几何信息以及强度信息合并到配准中。

结果：在三个实验中证明了该方法的鲁棒性和准确性。我们的方法的平均配准误差分别为1.17、1.33和1.20，而T1-T2，T1-PD和T2-PD配准的LMI分别为1.47、1.63和1.40。

讨论：在本文中，我们将3 D-FAST计算出的结构相似性用作条件信息，以将空间和几何线索编码为LMI。在所有这三个实验中，我们的方法显示出比基于信息论的普通注册方法更鲁棒和准确。