Medical Image Fusion Algorithm using m-PCNN

This program implements a medical image fusion algorithm based on m-PCNN (multi-channel PCNN). The algorithm represents an advanced image processing technique that integrates multiple channels of medical images to enhance both image quality and information content. Through the implementation of m-PCNN algorithm, we can more effectively capture detailed features and characteristics within images, thereby improving the accuracy and comprehensiveness of medical image diagnosis and analysis. The core implementation utilizes pulse-coupled neural networks with multi-channel input processing, where each channel corresponds to different medical imaging modalities or features. The algorithm demonstrates excellent noise resistance and image enhancement capabilities, effectively improving the visualization quality of medical images. Key technical components include neuronal linking mechanisms, dynamic threshold adjustments, and synchronized pulse bursts for optimal feature extraction. From a programming perspective, the implementation involves matrix operations for image data handling, iterative neural network simulations, and fusion rule applications. The development of this program holds significant importance in the field of medical image processing and analysis, potentially providing enhanced support for medical image diagnosis and treatment planning through improved image fusion quality and feature preservation.