PCNN Automatic Iterative Algorithm Based on 2D-OTSU Method for Image Segmentation

In this paper, we present the source code for a PCNN (Pulse-Coupled Neural Network) automatic iterative algorithm based on the 2D-OTSU method, designed specifically for image segmentation with demonstrated excellent performance. The implementation utilizes two-dimensional OTSU thresholding to automatically determine optimal segmentation parameters through variance maximization between foreground and background classes. The PCNN component employs pulse-coupled neurons that synchronize based on pixel intensity similarities, creating iterative firing patterns that progressively refine segmentation boundaries. We provide detailed explanations of the algorithmic principles and implementation steps, including key functions for matrix operations and neural network iteration control. Sample images and corresponding segmentation results are included to facilitate better understanding of the methodology. Furthermore, we discuss potential application domains and future research directions, enabling readers to further explore and apply this approach. Overall, this work offers comprehensive guidance for implementing PCNN automatic iteration with 2D-OTSU algorithm, aiming to advance research and applications in the field of image segmentation.