Dual-Threshold Image Segmentation Using Genetic Algorithm

This implementation employs a dual-threshold image segmentation method based on genetic algorithm, specifically utilizing the KSW approach. The KSW method represents a genetic algorithm-based image segmentation technique that operates by optimizing image thresholds to achieve effective segmentation with high accuracy and stability. In this methodology, the genetic algorithm is applied to search for optimal threshold combinations that effectively partition the image into distinct regions. The algorithm typically involves initializing a population of threshold pairs, evaluating their fitness using criteria like between-class variance or entropy measures, and applying genetic operators (selection, crossover, mutation) to evolve toward optimal solutions. This approach enables superior image segmentation and facilitates the extraction of critical feature information, thereby creating enhanced possibilities for subsequent image processing and analysis tasks. Key implementation aspects include chromosome encoding for threshold representation, fitness function design for segmentation quality evaluation, and parameter tuning for genetic operation efficiency.