Master-slave hierarchy local information driven fuzzy C-means clustering for noisy image segmentation

Local neighborhood information plays an important role in robust fuzzy clustering-related segmentation algorithms, and how to construct local information items is the key to robust fuzzy clustering. Based on existing local information constraints, this paper proposes a model to describe the hierarchy relationship of local neighborhood windows in the master-slave neighborhood model, which combines spatial distance with gray information to suppress the influence of noise on current pixel clustering, and can also well control the balance of noise suppression and detail preservation. Based on this model, this paper proposes a robust fuzzy clustering segmentation algorithm with master-slave neighborhood information constraints. When constraining the neighborhood pixels of a pixel (that is the master neighborhood pixels), the algorithm will further constrain the pixels in the neighborhood window around the master neighborhood pixel (that is the salve neighborhood pixels), thus enhancing the robustness of the algorithm. Experiments results show that the proposed algorithm has good segmentation performance and strong anti-noise performance, even significantly outperforms existing state-of-the-art robust fuzzy clustering-related algorithms in the presence of high noise.