Adaptive weighted PET/SPECT and MR medical image fusion based on nonsubsampled shearlet transform

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.