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

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Abstract

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.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Numbers 61671377), and the Natural Science Foundation of Shaanxi Province (2022JM-370). Wu and Wu would like to thank the anonymous reviewers for their constructive suggestions to improve the overall quality of the paper. Besides, Wu and Wu would like to thank the School of Electronic Engineering, Xi’an University of Posts & Telecommunications, Xi’an, China for financial support.

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    Chengmao Wu & Wen Wu

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Wu, C., Wu, W. Master-slave hierarchy local information driven fuzzy C-means clustering for noisy image segmentation. Vis Comput 40, 865–897 (2024). https://doi.org/10.1007/s00371-023-02821-1

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