Image Segmentation Program Using Genetic Algorithm Implemented in MATLAB

This program implements image segmentation using a genetic algorithm developed in MATLAB. Genetic algorithms are optimization techniques that mimic natural selection and genetic mechanisms to solve complex problems. In the context of image segmentation, the algorithm works by evaluating fitness functions and performing genetic operations (selection, crossover, mutation) to evolve toward optimal segmentation boundaries. The implementation typically involves encoding image regions as chromosomes, where each gene represents a segmentation parameter or pixel classification. Key MATLAB functions used may include population initialization (rand, randi), fitness calculation using image statistics (mean, std, entropy), and genetic operators (selection via roulette wheel or tournament, crossover with single/multi-point methods, mutation with probability control). This program serves as a practical example of how genetic algorithms can automatically discover optimal segmentation schemes based on image characteristics and requirements. The method finds extensive applications in image processing and computer vision domains, enabling improved understanding and analysis of image content through adaptive thresholding, region growing, or boundary detection approaches.