A multiobjective spatial fuzzy clustering algorithm for image segmentation

• Multiobjective spatial fuzzy clustering for image segmentation is proposed.
• The non-local spatial information of an image is introduced into fitness functions.
• The final solution is chosen by a cluster index with non-local spatial information.
• The proposed method can automatically evolve the number of clusters.
• Experiments on noisy images demonstrate the superiority of the proposed method.

This article describes a multiobjective spatial fuzzy clustering algorithm for image segmentation. To obtain satisfactory segmentation performance for noisy images, the proposed method introduces the non-local spatial information derived from the image into fitness functions which respectively consider the global fuzzy compactness and fuzzy separation among the clusters. After producing the set of non-dominated solutions, the final clustering solution is chosen by a cluster validity index utilizing the non-local spatial information. Moreover, to automatically evolve the number of clusters in the proposed method, a real-coded variable string length technique is used to encode the cluster centers in the chromosomes. The proposed method is applied to synthetic and real images contaminated by noise and compared with k-means, fuzzy c-means, two fuzzy c-means clustering algorithms with spatial information and a multiobjective variable string length genetic fuzzy clustering algorithm. The experimental results show that the proposed method behaves well in evolving the number of clusters and obtaining satisfactory performance on noisy image segmentation.

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