Abstract: PET/CT medical image fusion has very important application value for medical image analysis and diseases diagnosis. It is useful to improve the image content and accuracy by fusing PET/CT images. Aiming at PET/CT fusion problem, this paper proposes a self-adaption fusion algorithm of PET/CT based on dual-tree complex wavelet transform. Firstly, source PET and CT images after registration are decomposed low and high frequency sub-images using dual-tree complex wavelet transform (DTCWT). Secondly, according to the characteristics of low frequency sub-images concentrating the majority energy of the source image and determining the image contour, a fusion rule based on self-adaption Gaussian membership function is adopted in low frequency sub-band coefficients. Thirdly, in high frequency sub-images, according to the relation among region pixels and fuzziness, in the first layer of high-frequency component, Gaussian membership function and 3×3 field windows are used to fuse the high-frequency dual-tree complex wavelet coefficients. In the second layer of high-frequency component, regional variance fusion rule is used. Finally, in order to verify the validity and feasibility of the proposed algorithm, three experiments are done, comparison experiment of the proposed algorithm and other pixel-level fusion algorithms, the fusion effect evaluation experiment with information entropy, mean, standard deviation and mutual information, and comparison experiment with different fusion rules of dual-tree complex wavelet transform. The experimental results show that the proposed algorithm can improve the information entropy by 7.23%, and mutual information by 17.98%. That is to say the algorithm is an efficient fusion method of multimode medical image.

Key words: PET/CT, image fusion, dual-tree complex wavelet, Gaussian membership function, self-adaption

摘要： PET/CT医学图像融合对于图像分析及临床诊断具有重要的应用价值，通过融合PET/CT图像，可以丰富图像的信息量，提高信息准确度。针对PET/CT融合问题，提出了一个基于双树复小波的PET/CT自适应融合算法。对已配准的PET和CT图像进行双树复小波变换（dual-tree complex wavelet transform，DTCWT），得到低频分量和高频分量；根据低频图像集中了大部分源图像能量及决定了图像轮廓的特点，采用了自适应高斯隶属度函数的融合规则；在高频图像部分，考虑了图像相邻像素之间的相关性和模糊性问题，在第一层的高频分量上采用了高斯隶属度函数和3×3领域窗口相结合的融合规则，在第二层高频分量上采用了区域方差的融合规则。最后，为了验证算法的有效性和可行性，做了3个方面的实验，分别是该算法和其他像素级融合算法的比较实验，利用信息熵、均值、标准方差和互信息的融合效果评价实验，双树复小波变换中不同融合规则的比较实验。实验结果表明，该算法信息熵提高了7.23%，互信息提高了17.98%，说明该算法是一种有效的多模态医学影像融合方法。

关键词: PET/CT, 图像融合, 双树复小波, 高斯隶属度函数, 自适应