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Local Information-Driven Intuitionistic Fuzzy C-Means Algorithm Integrating Total Bregman Divergence and Kernel Metric for Noisy Image Segmentation

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Abstract

To improve the segmentation performance and anti-noise robustness of existing weighted kernel intuitionistic fuzzy clustering, a robust kernelized total Bregman divergence-based fuzzy local information clustering motivated by intuitionistic fuzzy information is proposed. In this algorithm, a kernelized total Bregman divergence is extended by a polynomial kernel function, and the corresponding intuitionistic kernelized total Bregman divergence is put forward to measure the difference between intuitionistic fuzzy sets. Then, the weighted local information is introduced into the objective function of intuitionistic fuzzy clustering, and the similarity between the current pixel and its neighborhood pixels is constructed to better describe the influence of neighborhood pixels on the current pixel. Finally, the square root of the deviation between the current pixel and the mean value of its neighboring pixels is used to adjust the local spatial information to improve the robustness to noise or outliers and further enhance the anti-noise ability of the algorithm. Experimental results show that the proposed algorithm has better clustering performance and stronger anti-noise robustness than existing state-of-the-art fuzzy clustering-related algorithms.

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Data Availability

The MR brain images generated and analyzed during this study are available in the Kaggle brain tumor dataset: https://www.kaggle.com/hasimdev/brain-mri-dataset. The natural images generated and analysed during this study are available in the Berkeley Segmentation Dataset: https://doi.org/10.1109/ICCV.2001.937655. The remote sensing images generated and analysed during this study are available in the UC Merced Land Use dataset: http://vision.ucmerced.edu/datasets/landuse.html and NWPU-RESISC45: http://www.escience.cn/people/JunweiHan/NWPU-RESISC45.html.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (62071378) (Grant No. 61671377, 51709228) and the Shaanxi Natural Science Foundation of China (Grant Nos. 2016JM8034, 2017JM6107, 2018JM4018, 2021JM-459, 2022JM-370).

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  1. School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an, 710121, People’s Republic of China

    Chengmao Wu, Congcong Huang & Jiajia Zhang

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CW was involved in the conceptualization, data curation, funding acquisition, methodology, project administration, resources, supervision, validation, visualization. CH contributed to the formal analysis, investigation, software, writing—original draft, writing—review and editing. JZ contributed to writing—review.

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Correspondence to Congcong Huang.

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Wu, C., Huang, C. & Zhang, J. Local Information-Driven Intuitionistic Fuzzy C-Means Algorithm Integrating Total Bregman Divergence and Kernel Metric for Noisy Image Segmentation. Circuits Syst Signal Process 42, 1522–1572 (2023). https://doi.org/10.1007/s00034-022-02175-4

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