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Local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation

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Abstract

Kernel fuzzy weighted local information C-means clustering is a widely used robust segmentation algorithm for noisy images. However, it cannot effectively solve the segmentation problem of blurred and noisy images. A local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation is proposed in this paper. Firstly, a local ternary pattern is used to extract the feature information of the blurred and noisy images; Secondly, the image feature information is embedded into the objective function of fuzzy local information clustering and an optimization model for blurred and noisy image segmentation is established. Thirdly, Lagrange multiplier method is used to solve this optimization problem, and a dual-level alternating iterative clustering algorithm for blurred and noisy image segmentation is obtained. Experimental results demonstrate that the proposed algorithm has better segmentation performance for blurred and noisy images than the latest robust fuzzy clustering-related algorithms, and its PSNR and ACC values increase by about 0.09 ~ 1.07 and 0.08 ~ 0.13, respectively.

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Data availability

The data used to support the findings of this study are available from the corresponding author upon request.

References

  1. Qureshi, I., Yan, J., Abbas, Q., Shaheed, K., Riaz, A.B., Wahid, A., Khan, M.W.J.J., Szczuko, P.: Medical image segmentation using deep semantic-based methods: a review of techniques, applications and emerging trends. Inform. Fusion 90, 316–352 (2023). https://doi.org/10.1016/j.inffus.2022.09.031

    Article  Google Scholar 

  2. Fasihi, M.S., Mikhael, W.B.: Overview of current biomedical image segmentation methods. In: 2016 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 2016, 803–808. IEEE (2016). https://doi.org/10.1109/CSCI.2016.0156

  3. Kucharski, A., Fabijańska, A.: CNN-watershed: a watershed transform with predicted markers for corneal endothelium image segmentation. Biomed. Signal Process. Control 68, 102805 (2021). https://doi.org/10.1016/j.bspc.2021.102805

    Article  Google Scholar 

  4. Shang, R., Liu, M., Jiao, L., Feng, J., Li, Y., Stolkin, R.: Region-level SAR image segmentation based on edge feature and label assistance. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2022). https://doi.org/10.1109/TGRS.2022.3217053

    Article  Google Scholar 

  5. Kumar, N., Kumar, H.: A fuzzy clustering technique for enhancing the convergence performance by using improved fuzzy C-means and particle swarm optimization algorithms. Data Knowl. Eng. 140, 102050 (2022). https://doi.org/10.1016/j.datak.2022.102050

    Article  Google Scholar 

  6. Kumar, P., Agrawal, R.K., Kumar, D.: Fast and robust spatial fuzzy bounded k-plane clustering method for human brain MRI image segmentation. Appl. Soft Comput. 133, 109939 (2023). https://doi.org/10.1016/j.asoc.2022.109939

    Article  Google Scholar 

  7. Ahmed, M.N., Yamany, S.M., Mohamed, N., Farag, A.A., Moriarty, T.: A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans. Med. Imaging 21(3), 193–199 (2002). https://doi.org/10.1109/42.996338

    Article  Google Scholar 

  8. Chen, S., Zhang, D.: Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 34(4), 1907–1916 (2004). https://doi.org/10.1109/TSMCB.2004.831165

    Article  Google Scholar 

  9. Cai, W., Chen, S., Zhang, D.: Fast and robust fuzzy C-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn. 40(3), 825–838 (2007). https://doi.org/10.1016/j.patcog.2006.07.011

    Article  MATH  Google Scholar 

  10. Krinidis, S., Chatzis, V.: A robust fuzzy local information C-means clustering algorithm. IEEE Trans. Image Process. 19(5), 1328–1337 (2010). https://doi.org/10.1109/TIP.2010.2040763

    Article  MathSciNet  MATH  Google Scholar 

  11. Gong, M., Liang, Y., Shi, J., Ma, W., Ma, J.: Fuzzy C-means clustering with local information and kernel metric for image segmentation. IEEE Trans. Image Process. 22(2), 573–584 (2012). https://doi.org/10.1109/TIP.2012.2219547

    Article  MathSciNet  MATH  Google Scholar 

  12. Ojala, T., Pietikainen, M., Maenpaa, T.: Multire solution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002). https://doi.org/10.1109/TPAMI.2002.1017623

    Article  Google Scholar 

  13. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19, 1635–1650 (2007). https://doi.org/10.1007/978-3-540-75690-3_13

    Article  MathSciNet  MATH  Google Scholar 

  14. Zhao, Q., Wang, H., Yue, Z.S., Meng, D.Y.: A deep variational Bayesian framework for blind image deblurring. Knowl.-Based Syst. 249, 109008 (2022). https://doi.org/10.1016/j.knosys.2022.109008

    Article  Google Scholar 

  15. Zeng, X., Dong, Q., Li, Y.: MG-CNFNet: a multiple grained channel normalized fusion networks for medical image deblurring. Biomed. Signal Process. Control 82, 104572 (2023). https://doi.org/10.1016/j.bspc.2023.104572

    Article  Google Scholar 

  16. Basar, S., Ali, M., Ochoa-Ruiz, G., Waheed, A., Rodriguez-Hernandez, G., Zareei, M.: A novel defocused image segmentation method based on PCNN and LBP. IEEE Access 9, 87219–87240 (2021). https://doi.org/10.1109/ACCESS.2021.3084905

    Article  Google Scholar 

  17. Lelandais, B., Ducongé, F.: Deconvolution regularized using fuzzy C-means algorithm for biomedical image deblurring and segmentation. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), 1457–1461 (2015). https://doi.org/10.1109/ISBI.2015.7164151

  18. Zohair, A.A., Ghazali, S.L.: A novel Zohair filter for deblurring computed tomography medical images. Int. J. Imaging Syst. Technol. 25(3), 265–275 (2015). https://doi.org/10.1002/IMA.22143

    Article  Google Scholar 

  19. Chen, L., Fang, F., Wang, T., Zhang, G.: Blind image deblurring with Local Maximum Gradient Prior. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 1742–1750 (2019). https://doi.org/10.1109/CVPR.2019.00184

  20. Zhang, X., Sun, Y., Liu, H., Hou, Z., Zhao, F., Zhang, C.: Improved clustering algorithms for image segmentation based on non-local information and back projection. Inf. Sci. 550(6), 129–144 (2021). https://doi.org/10.1016/j.ins.2020.10.039

    Article  MathSciNet  MATH  Google Scholar 

  21. Tang, Y., Ren, F., Pedrycz, W.: Fuzzy C-means clustering through SSIM and patch for image segmentation. Appl. Soft Comput. 87, 105928 (2020). https://doi.org/10.1016/j.asoc.2019.105928

    Article  Google Scholar 

  22. Gharieb, R.R., Gendy, G., Selim, H.: A hard C-means clustering algorithm incorporating membership KL divergence and local data information for noisy image segmentation. Int. J. Pattern Recogn. Artif. Intell. 32(04), 1850012 (2018). https://doi.org/10.1142/S021800141850012X

    Article  Google Scholar 

  23. Wang, Q., Wang, X., Fang, C., Jiao, J.: Fuzzy image clustering incorporating local and region-level information with median memberships. Appl. Soft Comput. 105, 107245 (2021). https://doi.org/10.1016/j.asoc.2021.107245

    Article  Google Scholar 

  24. Gao, Y., Wang, Z., Xie, J., Pan, J.: A new robust fuzzy C-means clustering method based on adaptive elastic distance. Knowl.-Based Syst. 237, 107769 (2022). https://doi.org/10.1016/j.knosys.2021.107769

    Article  Google Scholar 

  25. Ghosh, S., Hazarika, A.P., Chandra, A., Mudi, R.K.: Adaptive neighbor constrained deviation sparse variant fuzzy C-means clustering for brain MRI of AD subject. Vis. Inform. 5(4), 67–80 (2021). https://doi.org/10.1016/j.visinf.2021.12.001

    Article  Google Scholar 

  26. Zheng, X., Chen, T.: High spatial resolution remote sensing image segmentation based on the multiclassification model and the binary classification model. Neural Comput. Appl. (2021). https://doi.org/10.1007/s00521-020-05561-8

    Article  Google Scholar 

  27. Zhou, W., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004). https://doi.org/10.1109/TIP.2003.819861

    Article  Google Scholar 

  28. Zhang, L., Zhang, L., Mou, X., Zhang, D.: FSIM: a feature similarity index for image quality assessment. IEEE Trans. Image Process. 20(8), 2378–2386 (2011). https://doi.org/10.1109/TIP.2011.2109730

    Article  MathSciNet  MATH  Google Scholar 

  29. https://www2.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/resources.html#bsds500

  30. https://cocodataset.org/

  31. Hu, L., Pan, X., Tan, Z., Luo, X.: A fast fuzzy clustering algorithm for complex networks via a generalized momentum method. IEEE Trans. Fuzzy Syst. 30(9), 3473–3485 (2021). https://doi.org/10.1109/TFUZZ.2021.3117442

    Article  Google Scholar 

  32. Nie, F., Liu, C., Wang, R., Wang, Z., Li, X.: Fast fuzzy clustering based on anchor graph. IEEE Trans. Fuzzy Syst. 30(7), 2375–2387 (2021). https://doi.org/10.1109/TFUZZ.2021.3081990

    Article  Google Scholar 

  33. Akram, A.W., Alamgir, Z.: Distributed fuzzy clustering algorithm for mixed-mode data in Apache SPARK. J. Big Data 9(1), 121 (2022). https://doi.org/10.1186/s40537-022-00671-7

    Article  Google Scholar 

  34. Jha, P., Tiwari, A., Bharill, N., Ratnaparkhe, M., Mounika, M., Nagendra, N.: Apache Spark based kernelized fuzzy clustering framework for single nucleotide polymorphism sequence analysis. Comput. Biol. Chem. 92, 107454 (2021). https://doi.org/10.1016/j.compbiolchem.2021.107454

    Article  Google Scholar 

  35. Liu, H., Wang, H.O., Wu, Y., Xing, L.: Superpixel region merging based on deep network for medical image segmentation. ACM Trans. Intell. Syst. Technol. 11, 1–22 (2020). https://doi.org/10.1145/3386090

    Article  Google Scholar 

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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 Qi would like to thank the anonymous reviewers for their constructive suggestions to improve the overall quality of the paper. Besides, Wu and Qi 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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Authors and Affiliations

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

    Chengmao Wu & Xiao Qi

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  1. Chengmao Wu
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ChengmaoWu: Conceptualization, Writing-review and editing. Xiao Qi: Software, Methodology, Writing-originaldraft, Writing-reviewa and editing.

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Correspondence to Xiao Qi.

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Wu, C., Qi, X. Local feature driven fuzzy local information C-means clustering with kernel metric for blurred and noisy image segmentation. J Real-Time Image Proc 20, 116 (2023). https://doi.org/10.1007/s11554-023-01371-y

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