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

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..