PSO-Optimized Otsu Image Segmentation Algorithm

The PSO-optimized Otsu image segmentation algorithm presents an efficient approach for image segmentation tasks. This hybrid method integrates Particle Swarm Optimization with the traditional Otsu thresholding technique, where PSO serves as an optimization engine to determine the optimal segmentation threshold by evolving particle positions through iterative updates. Particle Swarm Optimization mimics collective behavior patterns observed in bird flocks, utilizing velocity and position update equations (v_i(t+1) = w*v_i(t) + c1*r1*(pbest_i - x_i(t)) + c2*r2*(gbest - x_i(t))) to explore the solution space efficiently. The Otsu method, a statistical-based adaptive thresholding approach, calculates the optimal threshold by maximizing inter-class variance (σ²_b = ω₀(μ₀-μ_T)² + ω₁(μ₁-μ_T)²) between foreground and background pixel distributions. In implementation, each particle in PSO represents a candidate threshold value, with fitness evaluation performed using Otsu's inter-class variance function. The algorithm typically involves initializing particle positions uniformly across the threshold range [0,255], updating velocities with inertia weights, and converging toward the global best solution through collaborative particle movement. This synergy enables enhanced handling of complex image characteristics, improving segmentation accuracy and robustness compared to standard Otsu implementation, particularly for images with bimodal histogram distributions where traditional methods may struggle with local optima.