中文摘要：

分析了Shannon互信息、Kullback-Leibler距离和Shannon不等式之间的相互关系，并根据不等式理论，提出了广义距离度量的新概念及其定义．在此基础上构造了多模态图像配准的一类新测度——算术一几何均值距离、Cauchy-Schwartz距离和Minkowski广义距离．从计算速度、噪声容忍性、测度函数图形的特点和图像窗口大小影响等几个方面，通过MR和PET医学图像的实验分析，验证了新配准测度的有效性。

英文摘要：

The connections between Shannon mutual information, Kullback-Leibler divergence and Shannon inequality are investigated. Based on these connections and inequality theory, a new concept of generalized divergence is proposed, and a corresponding definition is given. Thus a class of novel similarity measures for multimodal image registration is put forward, such as algorithm-geometry mean divergence, Cauchy-Schwartz divergence and Minkowski generalized divergence. The novel measures are applied to rigid registration of positron emission tomography （PET） magnetic resonance （MR） image pairs. Their performance is compared with mutual information as to the consumed time, sensitivity to noise and effects of different image window size. The results of tests indicate that some of the novel similarity functions can yield better performance and demonstrate that the proposed methods are efficient and effective.