Research On Image Segmentation Algorithms Based On Fuzzy Clustering

The main purpose of image segmentation is to extract the interesting regions from thebackground for further analysis. Image segmentation techniques have been widely used inmedical image processing, pattern recognition, computer vision and other fields. For theimages are ambiguous and uncertain inherently, image segmentation is a classic problem inimage processing. Many scholars have done a lot researches on image segmentation andproposed plenty of segmentation algorithms. The algorithm of image segmentation basedon fuzzy clustering is very important and has been used widely. The fuzzy clusteringalgorithm can properly describe the ambiguity of images, but some drawbacks of the classicmethod affect its widely application.Therefore, some researches are proceeding on the quite popular fuzzy C-means(FCM)algorithms based on fuzzy clustering. Several improved schemes of FCM are presented toavoid the shortcomings in image segmentation. The following key findings have been madein this paper:(1) It is a big drawback that the traditional fuzzy C-means algorithm is susceptive tonoises, this paper presents a fuzzy C-means algorithm that contains the space informationand membership constraints. By adding new constrained item into the energy function ofthe traditional fuzzy C-means algorithm, the algorithm is robust to salt and pepper noise,Gaussian noise and mixed noise. The new algorithm can obtain more sensible clusteringcenters. The membership of the pixels affected by noise can be properly modified, thereforethe pixels could be correctly classified. The item newly added is full free of empiricallyadjusted parameter. Comparing with the FCM algorithm, FCM_S algorithm, MOP_FCMalgorithm, robust FCM algorithm, RFCM algorithm and FLICM algorithm, the algorithm issuitable to segment the images that affect by noises and could provide better segmentationresults.(2) The details of images can’t be reserved effectively in the traditional fuzzy C-manesalgorithm. This paper proposes pyramid based fuzzy C-means algorithm. The pyramidstructure is introduced to rapid the approach and the adaptive threshold selection ofmembership grade avoids the inflexibility of human threshold. The constraint is added intothe traditional fuzzy C-means objective function to constrain the membership of pixel in thebottom-level by taking its up-level membership into account, the constraint also containsthe spatial information. The algorithm could cluster the marginal data more appropriately.The comparisons of image segmentation experiments with several algorithms, such as FCMalgorithm, pyramid FCM algorithm, MS_FCM, etc. show that the new algorithm can effectively preserve the detail information of the image and improve the quality of imagesegmentation.(3) In order to segment color image effectively, fuzzy C-means algorithm based on Sand V color components is proposed. Firstly calculate the distribution of S, V colorcomponents of the color image in the HSV color space, then cluster the statistics using thehistogram-based fast fuzzy C-means clustering algorithm, finally segment the color imageaccording to the target color number and the cluster centers obtained. The algorithm issimple and fast, doesn’t require any priori knowledge and could effectively extract thetarget region in the color images. The segment results show that this method can segmentthe color images effectively, owns fast speed and better segment results. The algorithm issimple and quick effective.