Study Of Image Segmentation Algorithm Based On Fuzzy Set And Spatial Information

Image segmentation is the process of obtaining the interesting object or region from a given image,which is an important step in image processing and image analysis, and it is also a more challenging problem. At present, image segmentation technology has been widely used in computer vision, image analysis, medical image processing, remote sensing technology and geographic information system and other fields. The majority of image segmentation methods can be classified into three categories: Edge-detection methods,region-growing methods and feature vector clustering methods. Edge-detection methods attempt to detect discontinuities in the model that form the closed boundaries of components in the image and processed with segmentation by detecting continuous surfaces which have similar geometrical properties. The region-growing algorithm is restricted by the order in which pixels are scanned and on the value of pixels which are first visited. This paper mainly studies the fuzzy C means clustering(FCM) algorithm,It is one of feature vector clustering methods and widely used in the research and application.FCM has been proven effectively for image segmentation and its success is mainly due to the introduction of fuzziness for the belongingness of each image pixels. Compared with crisp or hard segmentation methods, FCM is able to retain more information from the original image. However, the fuzzy C means clustering algorithm still has a lot of problems in image segmentation.For example, it is difficult to determine the number of clusters, the sensitivity to noise and outliers, and not to make full use of the spatial information of the image and so on.Therefore, the image segmentation effect when the fuzzy C means clustering algorithm deals with the noise image is not ideal.Through the background research and analysis with image segmentation, studying the present situation and the existing problems of a variety of image segmentation methods,we select fuzzy C-means(FCM) as the theoretical basis of several algorithms proposed in this paper.Aiming at the characteristics of image segmentation and the problems of fuzzy C means clustering algorithm in image segmentation, we improve and expand the fuzzy C mean clustering algorithm is improved and expanded. The research work in this paper is as follows:(1) Because the FCM algorithm only considers the pixel information of the image, the spatial information of the image is not taken into account, and it is extremely sensitive to the noise, so that the FCM algorithm can not be a good segmentation of the noise image. In order to solve this problem, an improved FCM algorithm is proposed, which is realized by converting the spatial neighborhood information of the image to a new similarity measure.(2) The standard FCM algorithm must be estimated by a priori knowledge to determine the number of clusters. Therefore, when the number of clusters is not given in advance, the automatic fuzzy C means clustering algorithm(AFCM) is used to divide the pixels into different regions. In order to get a better segmentation quality, an improved algorithm based on automatic fuzzy C means clustering algorithm is proposed, which combines the spatial information of the image to the membership function.(3) A robust fuzzy clustering image segmentation algorithm(RFCM) is proposed to deal with the noise image. Because the algorithm still uses the Euclidean distance calculation method, it is still lack of the ability to suppress noise and outliers. Therefore, this paper proposes a fuzzy clustering image segmentation algorithm based on kernel function of distance measurement, it further extends the RFCM algorithm, which extends RFCM to the corresponding KRFCM algorithm by kernel method. The algorithm to get a new objective function by measuring the non Euclidean distance of the robustness of the original data space.(4) Aiming at the disadvantage of the shadow of the C-means(SCM) algorithm- it does not make full use of the spatial information of image pixels, based on the traditional SCM algorithm, combining with image pixel local spatial information, we put forward a kind of improved algorithm which is called local spatial SCM algorithm. Because the improved algorithm considering the effect of neighborhood pixels, local space SCM(LSSCM) suppression algorithm has a better ability to suppress noise. However, when the noise of the image is very high, the neighborhood pixels of the image will have unnormal characteristic value. Based on the local space SCM algorithm,we proposes a clustering algorithm based on non local spatial information combined with SCM.