为解决多模医学图像融合边缘模糊,互补信息不充分的问题,提出一种基于改进的引导滤波和双通道脉冲耦合神经网络(PCNN)的医学图像融合算法。利用非下采样轮廓波对医学源图像进行变换,采用双通道PCNN融合图像的低频部分,将改进的拉普拉斯能量和作为双通道PCNN的激励输入,将改进的空间频率作为链接强度;采用改进的引导滤波算法融合图像的高频部分。融合后的低频和高频信号进行非下采样轮廓波变换逆变换即可得到融合图像。实验结果表明,多模医学图像融合中,所提算法有效保留了源图像的特征信息,并在互信息量、信息熵、空间频率等客观评价指标上取得了良好的效果。

This study proposes a medical image fusion algorithm based on improved guided filtering and dual-channel pulse coupled neural networks (PCNN) to solve the problems of blurring edge and complementary information insufficiency in current multimodal medical image fusion. First, medical source images are transformed with a non-subsampled contourlet, and the dual-channel PCNN is used to fuse the low-frequency sub-bands. The sum of the modified Laplacian energy is used as the input of the dual-channel PCNN, and the improved spatial frequency is considered as the connection strength. Then, improved guided filtering is used to fuse the high-frequency sub-bands of the source images. Finally, the fusion of the low-frequency sub-bands and that of the high-frequency sub-bands are inverted by the non-subsampled contourlet transforming to obtain the fused image. Experimental results show that the proposed algorithm effectively retains the characteristic information of the source images and objectively evaluates the mutual information, information entropy, and spatial frequency.