Study On The Application Of GAAC Algorithm In Medical Image Segmentation

Image segmentation is a classical problem in the field of image processing andanalysis. Medical image segmentation is an important application in imagesegmentation.Genetic algorithm (GA) is based on genetic theory and natural selection, which isa global optimization search algorithm. The algorithm combines the rules of survivalof the fittest in the process of biological evolution and random chromosome exchangemechanism within the group. The algorithm has good global search capability.Because of without using of feedback information in the system, it often leads to alarge number of useless redundancy iteration, the solving efficiency low. The localsearch ability is weak.Ant Colony algorithm (AC) is a heuristic algorithm based on population. Thismethod is derived from observation of real ant colony and their collective foragingbehavior. Ant colony algorithm makes full use of the positive feedback mechanism ofthe pheromone, and its local search ability is strong. Due to the lack of pheromone inthe initial time, the time of pheromone accumulation is long in the beginning, thealgorithm having slow speed of solving. With the scale of problem enlarging, thealgorithm search time is long, the solution speed slow, and easy to converge to the localoptimal solution and lead to premature.Genetic ant colony algorithm (GAAC) is the integration of GA and AC. Its basicidea is to learn from the advantages of the two algorithms in solving the optimalsolution to overcome their respective disadvantages, in order to achieve complementaryadvantages. The algorithm is better than genetic algorithm in the optimal solutionefficiency, is superior to the ant colony algorithm in the time efficiency, and is a kind ofnew heuristic algorithm in solving efficiency and time efficiency.