Image Segmentation Based On Ga-Fkcn

Clustering is a mathematic method which classifies things according to some criteria and rules. Since the borderline of the entities is often indefinite, it is more natural to utilize fuzzy theory to clustering. After Zadeh putting forward Fuzzy Theory, it is popular that using fuzzy theory to deal with the classification with fuzzy characters, then widely developing, and applying to the fields including medical diagnoses, pattern recognition and image processing etc..In this dissertation, the research status of fuzzy clustering and image segmentation is studied; a brief review of the history, basic theory and flow of genetic algorithm is presented; fuzzy Kohonen clustering network theory is illustrated. Based on those theories above, an improvement algorithm of image segmentation including pretreatment and the course of segmentation is presented. In the pretreatment, a new membership function and an improved fuzzy enhancement function are given, and then the amelioration of the improved algorithm is analyzed according to the comparison with traditional Pal method. During the course of image segmentation, a clustering algorithm based on GA-FKCN(Genetic Algorithm-Fuzzy Kohonen Clustering Network), which combine the character of ensemble with the self-organization of fuzzy Kohonen clustering network. Finally, the image segmentation algorithm based on GA-FKCN is proved more effective and better than traditional method through an experiment on computer.