Multi-Threshold Image Segmentation Based on Exponential Entropy, Logarithmic Entropy, and Tsallis Entropy Using Particle Swarm Optimization

Multi-threshold image segmentation using particle swarm optimization based on exponential entropy, logarithmic entropy, and Tsallis entropy provides an effective image processing methodology. Particle swarm optimization is a heuristic optimization algorithm that simulates bird flock foraging behavior to solve complex problems. In image segmentation, Tsallis entropy combined with exponential and logarithmic entropy measures can effectively determine boundaries between different regions in an image. The implementation typically involves calculating entropy values for different threshold combinations and optimizing these using PSO's position and velocity update equations. By setting multiple thresholds, the algorithm can segment images into more refined regions with greater precision. This method finds wide applications in image processing fields such as medical image analysis and object detection. The code implementation includes key functions for entropy calculation, threshold evaluation, and PSO optimization loops. Since the current version is unoptimized, it allows for time comparison experiments to evaluate both effectiveness and computational efficiency, providing baseline performance metrics for future optimization work.