Fuzzy image clustering incorporating local and region-level information with median memberships

Abstract Image segmentation with Fuzzy C-Means (FCM) clustering algorithm is a widely researched topic. In the literature, region-level information considers more redundancy information of image and becomes a more powerful FCM technique. However, noise pixels can be over-preserved via region-level information causing FCM produce fake segmentation areas. Fuzzification of partition is also vital in FCM and Kullback–Leibler (KL) information with membership mean template is successful to reduce fuzzification. However, existence of outliers influences final segmentation result due to the sum operation during calculation of mean template. In this paper, we propose an improved FCM with adaptive local and region-level information as well as KL information with locally median membership degrees. Firstly, followed by region-level information, a new distance measure incorporating both local and region-level information is proposed where local information of the image of region-level information is acquired to smooth the over-preserved noise pixels. Secondly, we renounce the sum operation in membership mean template and propose that locally median template is more reasonable to be considered as prior probability in KL information. Thirdly, adaptive constraints for conventional FCM and local as well as region-level information are introduced. In the beginning, differences between local variances of original image and image of region-level information in exponential form are computed. Then, reciprocals of the differences and the differences themselves are considered as constraints of conventional FCM and local as well as region-level information individually. Experiments of grayscale and color image segmentation show that the proposed method FALRCM (Fuzzy Adaptive Local and Region-level information C-Means) achieves better performance in terms of fuzzy partition coefficient (V PC ), fuzzy partition entropy (V PE ), Segmentation Accuracy (SA), mean Intersection-over-Union (mIoU), Peak Signal-to-Noise Ratio (PSNR) and visual effects compared with several state-of-the-art FCM variants. For example, in grayscale noise image segmentation, average V PC and mIoU are up to 99.92% and 97.59% with standard deviation of 0.05% and 2.46% respectively while the method with the closest performance provides V PC and mIoU of 95.27% and 91.76% with standard deviations of 15.59% and 3.53% respectively. The proposed method FALRCM achieves better robustness of noise and lower partition fuzzification. In the end, limitations are shown in discussion and application fields as well as future studies are proposed in conclusion part.