A novel medical image fusion method based on multi-scale shearing rolling weighted guided image filter

Abstract

Medical image fusion is a crucial technology for biomedical diagnoses. However, current fusion methods struggle to balance algorithm design, visual effects, and computational efficiency. To address these challenges, we introduce a novel medical image fusion method based on the multi-scale shearing rolling weighted guided image filter (MSRWGIF). Inspired by the rolling guided filter, we construct the rolling weighted guided image filter (RWGIF) based on the weighted guided image filter. This filter offers progressive smoothing filtering of the image, generating smooth and detailed images. Then, we construct a novel image decomposition tool, MSRWGIF, by replacing non-subsampled shearlet transform's non-sampling pyramid filter with RWGIF to extract richer detailed information. In the first step of our method, we decompose the original images under MSRWGIF to obtain low-frequency subbands (LFS) and high-frequency subbands (HFS). Since LFS contain a large amount of energy-based information, we propose an improved local energy maximum (ILGM) fusion strategy. Meanwhile, HFS employ a fast and efficient parametric adaptive pulse coupled-neural network (AP-PCNN) model to combine more detailed information. Finally, the inverse MSRWGIF is utilized to generate the final fused image from fused LFS and HFS. To test the proposed method, we select multiple medical image sets for experimental simulation and confirm its advantages by combining seven high-quality representative metrics. The simplicity and efficiency of the method are compared with 11 classical fusion methods, illustrating significant improvements in the subjective and objective performance, especially for color medical image fusion.

Keywords

• rolling weighted guided image filter
• multi-scale shearing rolling weighted guided image filter
• improved local energy maximum
• parameter adaptive pcnn model