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Cellular automata are simple models of computation which exhibit fascinatingly complex behavior. They have captured the attention of several generations of researchers, leading to an extensive body of work. The emphasis is mainly on topics closer to computer science and mathematics rather than physics, biology or other applications. Many related works were interested in cellular automata capacities in image processing, but all of them were confronted with the problem of increase of rules number towards the number of cells states. In this paper, we propose an original solution to avoid this problem, the objective is a segmentation by edge detection, applied to binary images, grey level images and real images. Comparisons are made with standard edge detector (Canny) and algorithms based on cellular automata. Obtained results are encouraging.