Abstract:Fuzzy c-means clustering is an effective method used in image segmentation, but it is corrupted by noise easily because of ignoring spatial contextual information and structure information.A neighbourhood weighted fuzzy c-means clustering method based on DCT subspace is proposed. This papper first applies the discrete cosine transform (DCT) on image patches combined with the idea of partitioning, it establishes a similarity measure model based on image pacthes and local information. Then defines the neighbourhood-weighted distance to replace the Euclidean distance in the objective function. Finally, applied this method to synthetic image with different noises, real-world images, as well as magnetic resonance images. The experimental results show that the proposed algorithm can obtain more precise segmentation results and has the stronger anti-noise property.