Image Segmentation Using Threshold Method and Particle Swarm Optimization

In this document, I present my work on image segmentation techniques, specifically focusing on threshold methods and Particle Swarm Optimization (PSO) algorithms. Both approaches are applicable to image segmentation tasks, where threshold methods provide a straightforward intensity-based segmentation technique, while PSO offers a more sophisticated optimization framework for determining optimal segmentation parameters. My implementation includes working code that demonstrates both approaches. The threshold method implementation uses Otsu's algorithm for automatic threshold selection, which calculates the optimal threshold by maximizing inter-class variance. The PSO component optimizes segmentation parameters by simulating particle movement through solution space, where each particle's position represents potential threshold values and fitness is evaluated using segmentation quality metrics. The code is designed with simplicity in mind, featuring modular functions for image preprocessing, threshold calculation, and segmentation evaluation. Key functions include pso_optimizer() for parameter optimization and adaptive_threshold() for dynamic thresholding based on local image characteristics. I've also experimented with hybrid approaches combining traditional techniques with evolutionary algorithms to improve segmentation accuracy. My research objective is to explore various image segmentation methodologies and identify the most effective approaches for practical applications. The code includes performance comparisons between methods and validation tests on standard image datasets.