Exploring various neighborhoods in Cellular Automata for image segmentation

This paper presents the first results obtained by exploring different neighborhoods in two-dimensional Cellular Automata applied for the difficult task of automatic image segmentation. Numerical experiments have been performed on several real-world and synthetic images for which the ground truth is known, being therefore able to compute the algorithm performance by comparing the obtained segmented image with the correct segmentation. To this purpose, the DICE coefficient has been used, which is one of the most popular similarity measures found in the literature. Obtained results bring valuable input that could help further improve the algorithms based on Cellular Automata applied to image segmentation.

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