MRI and PET/SPECT Image Fusion Based on Adaptive Weighted Guided Image Filtering

Aiming at the problems that blurred edge structure, loss of texture details, distortion and slow running speed in medical image fusion, a novel medical image fusion method based on adaptive weighted guided image filtering is proposed in this paper. First of all, the weight factor of the weighted guided image filtering is optimized by using the gradient operator, and the gradient operator weighted guided image filtering with better edge-preserving ability is obtained. The filter is used to decompose the MRI image into smooth layer and detail layer. Secondly, a soft threshold fusion rule is designed to fuse the smooth layer and the PET/SPECT image to obtain the fusion sub-image, which can retain the contour structure and color information of the original image. Finally, the selective enhancement of the detail layer of MRI image can improve the texture information, and then combine the enhanced detail layer with the fusion sub-image to obtain the final fusion image. The experimental results show that the subjective visual effect of the fused image contains the basic spatial structure, rich texture details and color information. The overall performance of the objective evaluation index is also better than the algorithm compared.