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Neighbourhood weighted fuzzy c-means clustering algorithm for image segmentation

Neighbourhood weighted fuzzy c-means clustering algorithm for image segmentation

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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.

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