IR image segmentation based on the cellular automata model with immune strategy

Antibody, the core of natural immune system, has incredible abilities to recognize and kill pathogens or invaders, which provide a faster memory response if they reappear. Each pixel of segmentation image could be described as a live cell that belongs to a special race, and the levels of segmentation determine the amount of races. Additionally, every cell has two types of antibodies: innate antibody and acquired antibody, the former of which is inhered from the error of mean cluster operations. The antibodies of partial cell communities are optimized under the immune strategy, and while the cells of communities, which are considered as cellular automata models, are evolving and progressing. After evolution for many generations, the optimal result could be accessed by recognitions and selections. The segmentation experiments of IR image prove the method to be a fast searching algorithm in solution space and have a self-improving ability.