目的 由于获取医学图像的原理和设备不同，不同模式所成图像的质量、空间与时间特性都有较大差别，并且不同模式成像提供了不互相覆盖的互补信息，临床上通常需要对几幅图像进行综合分析来获取信息。方法 为了提高对多源图像融合信息的理解能力，结合Contourlet变换在多尺度和多方向分析方法的优势，将Contourlet变换应用于医学图像融合中。首先将源图像经过Contourlet变换分解获得不同尺度多个方向下的分解系数。其次通过对Contourlet变换后的系数进行分析来确定融合规则。融合规则主要体现在Contourlet变换后图像中的低频子带系数与高频子带系数的优化处理中。针对低频子带主要反映图像细节的特点，对低频子带系数采用区域方差加权融合规则；针对高频子带系数包含图像中有用边缘细节信息的特点，对高频子带系数采用基于主图像的条件加权融合规则。最后经过Contourlet变换重构获得最终融合图像。结果 分别进行了基于Contourlet变换的不同融合规则实验对比分析和不同融合方法实验对比分析。通过主观视觉效果及客观评价指标进行评价，并与传统融合算法进行比较，该算法能够克服融合图像在边缘及轮廓部分变得相对模糊的问题，并能有效地融合多源医学图像信息。结论 提出了一种基于Contourlet变换的区域方差加权和条件加权融合算法。通过对CT与MRI脑部医学图像的仿真实验表明，该算法可以增加多模态医学图像互补信息，并能较好地提高医学图像融合的清晰度。

Objective Because of a different imaging mechanism, different modality medical images provide a variety of characteristics about image quality, space, and non-overlay complementary information. In clinical use, we need to analyze the result of multimodal medical images. Method In order to use medical images effectively and reasonably, a medical image fusion algorithm is proposed, combining the advantages of multi-scale and multiple directions in the Contourlet transformation. First, multi-scale and multiple directions decomposition coefficients are obtained through Contourlet transformation. Second, fusion rules are proposed by analyzing the characteristics of Contourlet transformation coefficients. An optimized image fusion rule is proposed in low frequency sub-band coefficients and high frequency sub-band coefficients. For low frequency sub-band coefficients, the weighted regional variance fusion rule is adopted in view of the image detail characteristics. The high frequency sub-band coefficients are fused by a condition-weighted rule of the main image in view of the edge detail characteristics. Finally, the final fusion image is acquired through the Contourlet inverse transformation. Result Different fusion rules based on Contourlet transformation and different fusion methods are analyzed. The fusion results are analyzed and compared with the measurement of human visual system and objective evaluation. Compare the new fusion method with other classical fusion algorithm to confirm the advantages of the new method. The experimental results show that the proposed algorithm is effective in retaining the original images' information and reserving the edge features successfully. Conclusion A medical image weighting fusion algorithm is proposed based on Contourlet transformation. Medical images, including CT and MRI, are used for the experiments. The results show that the complementary information of medical image can be highlighted and the image definition has improved significantly.