Full-parameter adaptive fuzzy clustering for noise image segmentation based on non-local and local spatial information

Abstract

The fuzzy clustering C-means (FCM) algorithm is a compelling image segmentation method in image segmentation. However, the algorithm is less robust for images containing noise. This paper proposes a non-local full-parameter adaptive spatial information combined with a local fuzzy factor for noisy image segmentation. Firstly, we realize the adaptive calculation of the smoothing parameter, search term window, and neighborhood window in the non-local spatial information by defining the smoothness and designing the adaptive matching function. Secondly, the image’s non-local and local spatial information is considered comprehensively to reduce noise interference and segmentation ambiguity. Finally, the weighted average membership linking is used as the denominator of the objective function to reduce the number of iterations. The results in synthetic noise image and color image segmentation experiments show that the proposed algorithm has outstanding performance in various evaluation metrics and visual effects, outperforming most other variants of fuzzy clustering-based algorithms.