Ant Colony Clustering Algorithm for Image Segmentation

I developed an image segmentation program based on ant colony clustering algorithm for my graduation project. The program's primary objective is to process standard images and MRI medical images, achieving impressive segmentation results. In the implementation, the program first performs image preprocessing operations including noise reduction and contrast enhancement using filtering techniques. Subsequently, the ant colony clustering algorithm is applied to segment the image by separating different regions. This algorithm's key advantage lies in its ability to automatically identify and segment distinct regions based on image characteristics without requiring pre-set thresholds or manual region selection. The core algorithm simulates ant behavior patterns where virtual "ants" distribute across the image pixels, forming clusters through pheromone trails that represent region boundaries. Through testing on both standard images and MRI images, the program demonstrated strong adaptability and accuracy across different image types. Beyond segmentation, the program incorporates additional image processing capabilities such as edge detection using gradient-based operators and image reconstruction functions. The implementation utilized MATLAB's image processing toolbox for basic operations while custom-coding the ant colony optimization logic. Overall, this program represents a significant achievement from my graduation research, showing substantial application potential in the field of image processing and computer vision.