Image Segmentation Using Genetic Algorithm in MATLAB

This program implements image segmentation using genetic algorithms through MATLAB. Image segmentation is a fundamental technique that partitions digital images into distinct regions or segments, enabling more effective analysis and interpretation of different image components. Genetic algorithms represent a class of evolutionary optimization techniques inspired by natural selection and genetic mechanisms, including selection, crossover, and mutation operations. The MATLAB implementation employs key genetic algorithm components such as chromosome encoding of segmentation parameters, fitness function evaluation based on segmentation quality metrics, and iterative population evolution to converge toward optimal segmentation thresholds. The program utilizes MATLAB's image processing toolbox for preprocessing and implements custom genetic operators to efficiently search the solution space. This solution helps researchers and practitioners achieve superior results in image processing applications by automatically determining optimal segmentation parameters that might be difficult to identify through manual methods. The genetic algorithm approach proves particularly valuable for complex images where traditional segmentation methods may underperform, ultimately enhancing research accuracy and workflow efficiency in computer vision and medical imaging applications. Key implementation features include customizable population size, mutation rates, and termination criteria, along with visualization tools to monitor the algorithm's convergence progress and final segmentation results.