Improved Ant Colony Algorithm Based On Image Edge Detection Of Medical CT

Image edge is one of the most important basic features of digital image.Image edge detection is very important in image processing and computer vision processing.Therefore,it is very important to find out the suitable method to extract the edge of the image.Ant colony algorithm(ACO)is a kind of combination optimization algorithm based on ant colony's foraging behavior rule in the nature,but the ant colony system is limited,but the whole ant colony system can accomplish the complex tasks that a single individual can not accomplish.Shuffled frog leaping algorithm(SFLA)is a by natural biological imitation inspired ACO evolutionary algorithm and the algorithm has good ability of local search.This paper is mainly to the deficiencies of the traditional ant colony algorithm is easy to fall into local optimum,climbing thought improved the traditional ant colony algorithm using SFLA group,in order to further improve the performance of ant colony algorithm of adaptive mechanism previously proposed to improve and optimize the appropriate of ant colony algorithm,the experimental results show that,the proposed image edge detection algorithm based on improved ant colony algorithm,the simulation time is short,the rich edge information for edge detection,adaptive ant colony algorithm is superior to the edge detection and ant colony algorithm to separate the individual,and it is proved that the algorithm has good performance in image edge detection.In this paper,the characteristics is that it can be shuffled frog leaping algorithm and optimization of ant colony algorithm,the two kinds of bionics algorithm has the advantages of good combination together,and its application in the medical CT image edge detection.Although the ant colony algorithm has many people put used in image edge detection,but the adaptive mechanism and ant colony algorithm and shuffled frog leaping algorithm three mixed application and image edge detection is the first case.