Research On Image Segmentation Algorithm Of Fuzzy C-means Clustering Based On Optimization

Image segmentation is a key preprocessing technology in the field of computer vision and pattern recognition.Traditional segmentation methods have their limitations and are no longer suitable for today's diversified application needs.Therefore,image segmentation is still an important hot topic worthy of further study by scholars.Images can carry more information,because of its complexity has led some pixels classification is difficult to choose,because of the Fuzzy c-means clustering(Fuzzy C-mean clustering,FCM algorithm combined with Fuzzy theory is good at processing is uncertain and Fuzzy characteristics of the image information and is widely used,at the same time the traditional FCM algorithm,there are some inevitable limitations need to be improved.Therefore,this paper mainly focuses on the shortcomings of FCM algorithm,such as random initialization of clustering center,manual setting of classification category number,poor segmentation performance and poor noise resistance caused by not taking spatial information into account,and so on.The two improvement methods proposed in this paper for different aspects of FCM algorithm are as follows:Method 1: in chapter 3,FCM algorithm randomly initializes clustering center,which will affect clustering performance.In this paper,the final threshold output by combining the gravity search algorithm and particle swarm optimization algorithm is used as the initial clustering center of FCM algorithm to avoid falling into local optimization.Secondly,with the advantage of kernel function,the limitation of FCM algorithm on data non-linear processing ability is broken,and the segmentation performance of the whole algorithm is improved.Method 2: in the fourth chapter in view of the FCM algorithm with gaussian noise and salt and pepper noise image segmentation result unsatisfactory,even when high pollution levels for fault segmentation,this phenomenon is proposed based on histogram the membership degree of fuzzy c-means clustering image segmentation algorithm of filtering,in one dimensional image histogram peak point according to certain rules take objective to determine the classification number and can make the results more close to the real structure;Then on this basis,through the FCM algorithm eventually loop iterative output filter of the membership degree matrix way,at the same time without increasing the complexity of algorithm considering the membership degree of neighborhood information,this paper adopts integral figure to accelerate the nonlocal average filtering makes it redrawn to enhance the noise resistance and improve the segmentation accuracy,the purpose of through the result of the composite image segmentation using the quantitative analysis and comparison of the segmentation accuracy SA index and the experimental verification of the real figure,show that this algorithm is in effective denoising and segmentation is more accurate,edge profile isrelatively smooth,has certain advantages.