Study On Image Segmentation Algorithm Based On Auto-adaption Fuzzy Clustering Analysis

Image segmentation is an important research field of digital image processing, and is one of the hot issues in the study of it. Image segmentation is just to segment an image into different sub-images with different characters. It is important significance for image feature extraction, image analysis and description, image recognition. At present, there are many kind of image segmentation method. This paper put forward two based on adaptive fuzzy clustering image segmentation algorithm.The clustering analysis is an unsupervised classification method..Clustering analysis is always carried out in the condition with no pre-known knowledge. Optimizing deeply clustering algorithms will not only help to perfect its theory, but also its popularization and application.In this paper, then clustering algorithms and image segmentation by clustering were researched in depth according to the characteristics of clustering. Through selection and improvement of clustering alsorithms, use FCM algorithm and Mean Shift algorithm, two image segmentation algorithms based on auto-adaption clustering analysis are proposed. The main contents of the thesis includes:(1) Fuzzy C-means algorithm is one of the widely applied fuzzy algorithms at present. Based on the analysis of advantages and disadvantages of the self-adaptive FCM image segmentation algorithm, an improved self-adaptive fuzzy C-means clustering algorithm is proposed. First of all, themethod adopts an initial algorithm to assure the initial searching scope of genetic algorithm.Then improvements are appropriately made on parameter.Lastly step of the new algorithm is proposed.The method solves the limitation of converging to the local infinitesimal point in medical image segmentation, and adopts the initial algorithm to assure the initial searching scope of genetic algorithm which is better accommodable than standard genetic algorithm with fuzzy C-means clustering, speeding up the convergence of genetic algorithm.Contrast with results of experiment, the method is better than standard genetic algorithm fused with fuzzy C-means clustering.(2) Mean shift algorithm is a no nparametric statistical method for seeking the nearest mode of a point sample distribution, adaptive bandw ith analysis methods are proposed, he expermients prove that, compared with the fixed bandwidth mean shift clustering algorithm,the proposed method can get better classification reslts and higher quality.