Abstract
Medical image fusion improves clinical interpretation and analysis by combining the complementary information of multimodal images into one that leads to more accurate diagnosis and treatment planning. This article presents a novel feature-level medical image fusion (FMIF) method using a structural gradient-based decomposition, which provides uncorrelated structural and textural components. A feature codebook obtained from multiple low-scale features followed by clustering and choose-max with consistency verification rule is applied to fuse structural components. An optimized pulse-coupled neural network is utilized to fuse texture component using a differential evolution algorithm, which helps to improve the model efficiency and retain the natural response of pixel activity. The fusion performance of the proposed method is explored on a large data set of neurological images. The experimental result demonstrates that the proposed method provides better fusion results and outperforms the state-of-the-art fusion approaches with enhanced visual quality and computational parameters.
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