Instruments and Systems: Monitoring, Control, and Diagnostics Annotation << Back Comparison of Fuzzy Image Processing Methods YU.YU. GROMOV, P.I. KARASEV, M.YU. TITOV, SARI FARAH ABBAS Image segmentation is the process by which an image is divided into areas with similar features. Many approaches have been proposed for image segmentation, but the fuzzy C-Means method is commonly used because it gives better results for a large class of images. However, using this method is not suitable for images with noise and is laborious compared to other methods. For this reason, many other methods have been proposed to improve the disadvantages of image segmentation using fuzzy C-Means. Techniques such as fuzzy C-Means overcome the noise problem that persists with the FCM method. Fuzzy C-means introduces the concept of no cluster membership. The use of possible C-Means clustering is shown, which relaxes the column constraint in the FCM method so that the membership matrix better reflects the typicality of a particular data point in the cluster and noise can be avoided. A comparison is made of these clustering methods based on the runtime and the fidelity function for each method applied to the different types of images in question. Keywords: clustering, segmentation, C-Means, fuzzy, images. DOI: 10.25791/pribor.7.2021.1279 Pp. 55-61.