Medical image fusion is an essential method of combining complementary information from multiple imaging modalities, like Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET), to increase diagnostic precision. In this work, a new fusion method is introduced based on Multi-Layer Adaptive Curvature Filtering (MLACF) and Pulse Coupled Neural Network (PCNN). The MLACF algorithm splits images into small-scale, large-scale, and background parts while maintaining structural and edge information. The PCNN-based fusion approach then fuses the decomposed elements to improve feature preservation and visual perception. The method is tested based on several image fusion performance measures, such as entropy, structural similarity index (SSIM), Q_AB/F, and feature mutual information (FMI). Experimental results show that the method performs well in preserving key diagnostic details, and its performance outshines that of traditional fusion methods.