PCNN Pulse Coupled Neural Network Image Segmentation with Maximum Cross-Entropy Optimization

The following MATLAB code provides an advanced implementation of Pulse Coupled Neural Network (PCNN) image segmentation using maximum cross-entropy optimization to determine the optimal iteration count. This implementation demonstrates superior performance in effectively partitioning images while enhancing processing accuracy and computational efficiency. The algorithm incorporates maximum cross-entropy calculation at each iteration to automatically determine the stopping criterion, ensuring optimal segmentation results without manual parameter tuning. The code employs PCNN's biological modeling approach, simulating pulse transmission and interactions between neurons to achieve sophisticated image segmentation and feature extraction. Key functions include neuron state updating, linking modulation, and pulse firing mechanisms that mimic biological visual cortex processing. This PCNN-based method holds significant potential in various image processing applications, including medical image analysis, target recognition, and content-based image retrieval systems. The integration of maximum cross-entropy optimization makes this MATLAB implementation particularly valuable for researchers and practitioners working with adaptive image segmentation algorithms that require automatic iteration control and robust performance across diverse image types.