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.
Multimodal medical image fusion can provide comprehensive and rich information for doctors' diagnosis. Aiming at the problems of traditional fusion algorithms, such as PET/SPECT color distortion, insufficient MR texture and timeconsuming, this paper proposes a new multi-modal medical image fusion algorithm. Firstly, a nonsubsampled shearlet transform (NSST) was introduced to perform multi-scale decomposition of the source image to obtain low frequency and high frequency subbands. Then, since the low frequency subband image contains most of the intensity energy of the source image, which is divided into high energy region and low energy region according to the maximum between-class variance method, and the adaptive weighted fusion rule is proposed, which is beneficial to the high fidelity of the fused image and the visual effect is better. High-frequency subband have strong sparsity characteristics, adopting the maximum value fusion rule, and the image texture after fusion is clear. Finally, inverse NSST is performed on the fused low-frequency and high-frequency subbands to obtain the fused image. Compared with the representative medical image fusion algorithms in recent years, good results have been obtained in evaluation and computational efficiency.
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