Image Segmentation Using Genetic Algorithm (MATLAB Implementation)

In this document, we demonstrate how to perform image segmentation using genetic algorithms. Image segmentation is a technique that partitions an image into distinct regions sharing similar attributes or characteristics. Applying genetic algorithms to image segmentation enables better understanding of different image components and facilitates extraction of target objects or regions of interest.

The genetic algorithm is an optimization method that simulates natural evolutionary processes. It progressively optimizes solutions by mimicking genetic operations such as selection, crossover, and mutation. For image segmentation, genetic algorithms help identify optimal partitioning schemes where pixels within each region exhibit similar features through fitness function evaluation and population evolution.

To implement genetic algorithm-based image segmentation, we utilize the MATLAB programming environment. MATLAB provides comprehensive image processing tools and functions, including 'imread' for image loading, 'graythresh' for threshold comparison, and custom functions for chromosome encoding/decoding. The shared MATLAB source code includes population initialization, fitness calculation based on inter-class variance, roulette wheel selection, single-point crossover, and mutation operations with configurable parameters.

This document aims to support your learning and application of genetic algorithms in image segmentation tasks. For any questions or further assistance, please feel free to contact us. The implementation demonstrates practical integration of optimization algorithms with digital image processing techniques.