Threshold image segmentation based on granular immune algorithm

Image segmentation is an important processing step in many image, video and computer vision applications. Artificial Immune Systems (AIS) is a diverse area of research that attempts to bridge the divide between immunological and engineering. In this paper, we present a threshold method based on granular immune algorithm (GIA) for image segmentation, which includes granular hierarchy and immunological mechanism. Based on two granular hierarchies, the method can not only execute multi-point parallel search from local to global searching field but also find better solutions with small generation and mean numbers of function values. So this method has better performance in stabilization and convergence that GA. Our experimental results indicate that the proposed method here is very suitable for image segmentation.