Neighbourhood weighted fuzzy c‐means clustering algorithm for image segmentation

Fuzzy c‐means (FCM) clustering algorithm has been widely used in image segmentation. In this study, a modified FCM algorithm is presented by utilising local contextual information and structure information. The authors first establish a novel similarity measure model based on image patches and local statistics, and then define the neighbourhood‐weighted distance to replace the Euclidean distance in the objective function of FCM. Validation studies are performed on synthetic and real‐world images with different noises, as well as magnetic resonance brain images. Experimental results show that the proposed method is very robust to noise and other image artefacts.