基于3D NSDST和改进空间频率相结合的MRI/PET图像融合

MRI/PET image fusion based on 3D NSDST and improved spatial frequency

为解决三维核磁共振成像(Magnetic Resonance Imaging,MRI)与正电子发射断层成像(Positron Emission Tomography,PET)融合细节表达不足及能量信息不完善问题,提出了一种基于三维非下采样离散剪切波变换(3D Nonsubsampled Discrete Shearlet Transform,3D NSDST)和改进空间频率(spatial frequency,SF)相结合的图像融合方法。利用3D NSDST将MRI图像和PET图像分解为一个低频子带和若干个高频子带。低频子带取用改进SF的融合策略,自适应调节图像块的大小,同时考虑了三维空间内二十六邻域的体素信息,引入了三维拉普拉斯能量和来保留细节信息。高频子带取用脉冲耦合神经网络(Pulse Coupled Neural Network,PCNN)的融合策略,将三维拉普拉斯能量和作为输入,并用三维梯度能量作为链接强度来调节神经元。最后经3D NSDST逆变换重构图像,实现MRI/PET图像融合。实验结果表明,3D NSDST和改进空间频率相结合的融合策略可以有效保留图像中的细节信息,同时不会影响图像的整体对比度,在主观评价和客观评价上与已有算法相比具有一定优势。

In order to solve the problems of insufficient detail expression and incomplete energy information in 3D Magnetic Resonance Imaging(MRI)and 3D Positron Emission Tomography(PET)fusion,an image fusion method based on 3D Nonsubsampled discrete shearlet transform and improved spatial frequency was proposed.3D NSDST is used to decompose MRI image and PET image into one low-frequency sub-band and several high-frequency sub-band.The low-frequency sub-band adopts the improved spatial frequency(SF)fusion strategy to adaptively adjust the size of the image block,and takes into account the voxel information of 26 neighborhood in three-dimensional space,and introducesweight sum of three-dimensional modified Laplacian to retain energy information.The high frequency subband adopts the fusion strategy of pulse coupled neural network(PCNN).The weight sum of three-dimensional modified Laplacian is used as the input,and the energy of three-dimensional gradient is used as the link strength to adjust the neurons.Finally,the 3D NSDST inverse transform is used to reconstruct the image and realize the MRI/PET image fusion.Experimental results show that the fusion strategy combining 3D NSDST and improved spatial frequency can effectively preserve the detail information in the image without affecting the overall contrast of the image.It has certain advantages compared with existing algorithms in subjective and objective evaluation..