A heuristic solution for noisy image segmentation using Particle Swarm Optimization and Fuzzy clustering

Introducing methods that can work out the problem of noisy image segmentation is necessary for real-world vision problems. This paper proposes a new computational algorithm for segmentation of gray images contaminated with impulse noise. We have used Fuzzy C-Means (FCM) in fusion with Particle Swarm Optimization (PSO) to define a new similarity metric based on combining different intensity-based neighborhood features. PSO as a computational search algorithm, looks for an optimum similarity metric, and FCM as a clustering technique, helps to verify the similarity metric goodness. The proposed method has no parameters to tune, and works adaptively to eliminate impulsive noise. We have tested our algorithm on different synthetic and real images, and provided quantitative evaluation to measure effectiveness. The results show that, the method has promising performance in comparison with other existing methods in cases where images have been corrupted with a high density noise.