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Guided filter-driven kernel fuzzy clustering with local information for noise image segmentation

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Abstract

Fuzzy local information clustering is the most widely robust segmentation methods, but it is only suitable for image corrupted by certain intensity noise. Later, although fuzzy local information clustering integrated guided filter is improved the ability of suppressing noise, it still cannot meet the needs of image with high noise. This paper proposed a novel robust fuzzy local information clustering combined kernel metric with guided filter. Firstly, guided filter is introduced into fuzzy local information clustering with kernel metric (KWFLICM), and a novel multiple objective optimization model for fuzzy clustering is constructed. Secondly, the optimization model is solved by Lagrange multiplier method, and the iterative algorithm for image segmentation is presented. Experimental results show that the proposed algorithm has better segmentation performance and robustness than existing state of the art guided filter-driven fuzzy clustering with local information.

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References

  1. Ahmed MN, Yamany SM, Mohamed N (1999) A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21(3):193–199. https://doi.org/10.1109/42.996338

    Article  Google Scholar 

  2. Alonso F, Algorri M.-E, Fiores-Mangas F. (2004) Composite index for the quantitative evaluation of image segmentation results. The 26th annual international conference of the IEEE engineering in medicine and biology society, pp 1794–1797. https://doi.org/10.1109/IEMBS.2004.1403536

  3. Bai XZ, Zhang YX, Liu H (2018) Intuitionistic center-free FCM clustering for MR brain image segmentation. IEEE J Biomed Health Inform 23(5):2039–2051. https://doi.org/10.1109/JBHI.2018.2884208

    Article  Google Scholar 

  4. Boskovitz V, Guterman H (2010) Adaptive neuro-fuzzy for image segmentation and edge detection. Fuzzy Syst 10(2):247–262. https://doi.org/10.1109/91.995125

    Article  Google Scholar 

  5. Cai WL, Chen SC, Zhang DQ (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recogn 40(3):825–838. https://doi.org/10.1016/j.patcog.2006.07.011

    Article  MATH  Google Scholar 

  6. Cao HB, Deng HW, Wang YP (2012) Segmentation of M-FISH images for improved classification of chromosomes with an adaptive fuzzy c-means clustering algorithm. Fuzzy Syst 20(1):1–8. https://doi.org/10.1109/TFUZZ.2011.2160025

    Article  Google Scholar 

  7. Chen SC, Zhang DQ (2004) 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. https://doi.org/10.1109/TSMCB.2004.831165

    Article  Google Scholar 

  8. Chen LC, Papandreou G, Kokkinos L (2018) DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848. https://doi.org/10.1109/TPAMI.2017.2699184

    Article  Google Scholar 

  9. Cheng MM, Zhang GX, Mitra NJ (2015) Global contrast based salient region detection. Comput Vis Pattern Recognit 37(3):569–582. https://doi.org/10.1109/TPAMI.2014.2345401

    Article  Google Scholar 

  10. Choy SK, Lam SY, Yu KW (2017) Fuzzy model-based clustering and its application in image segmentation. Pattern Recogn 68:141–157. https://doi.org/10.1016/j.patcog.2017.03.009

    Article  Google Scholar 

  11. Chuang KS, Tzeng HL, Chen S (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30(1):9–15. https://doi.org/10.1016/j.compmedimag.2005.10.001

    Article  Google Scholar 

  12. Dagher I. (2015) Fuzzy clustering with optimized-parameters multiple Gaussian kernels. IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 1–6. https://doi.org/10.1109/FUZZ-IEEE.2015.7337804

  13. Fan F, Ma Y, Li C (2017) Hyperspectral image denoising with super-pixel segmentation and low-rank representation. Inf Sci 397:48–68. https://doi.org/10.1016/j.ins.2017.02.044

    Article  Google Scholar 

  14. Feng F, Xu SJ, Meng YB (2018) Image segmentation based on high-order MRF model with robust local spatial information. 2018 Chinese automation congress (CAC), pp 4124–4128. https://doi.org/10.1109/CAC.2018.8623471

  15. Gharieb RR, Gendy G, Abdelfattah A (2017) Adaptive local data and membership based KL divergence incorporating c-means algorithm for fuzzy image segmentation. Appl Soft Comput 59:143–152. https://doi.org/10.1016/j.asoc.2017.05.055

    Article  Google Scholar 

  16. Gong MG, Liang Y, Shi J (2013) Fuzzy c-means clustering with local information and kernel metric for image segmentation. IEEE Trans Image Process 22(2):573–584. https://doi.org/10.1109/TIP.2012.2219547

    Article  MathSciNet  MATH  Google Scholar 

  17. Guo YH, Sengur A (2013) A novel color image segmentation approach based on neutrosophic set and modified fuzzy c-means. Circuits Syst Signal Process 32:1699–1723

    Article  MathSciNet  Google Scholar 

  18. Guo L, Chen L, Chen CLP (2016) Image guided fuzzy clustering for image segmentation. IEEE international conference on systems, man, and cybernetics (SMC): 4271–4276. https://doi.org/10.1109/SMC.2016.7844902

  19. Guo L, Chen L, Wu YW (2017) Image guided fuzzy c-means for image segmentation. Int J Fuzzy Syst 19:1660–1669. https://doi.org/10.1007/s40815-017-0322-1

    Article  MathSciNet  Google Scholar 

  20. Guo L, Chen L, Chen CLP (2018) Integrating guided filter into fuzzy clustering for noisy image segmentation. Digit Signal Process 83:235–248. https://doi.org/10.1016/j.dsp.2018.08.022

    Article  Google Scholar 

  21. Ham B, Cho M, Ponce J (2015) Robust image filtering using joint static and dynamic guidance. IEEE conference computer vision and pattern recognition (CVPR), pp 4823–4831. https://doi.org/10.1109/CVPR.2015.7299115

  22. Happ PN, Ferreira RS, Costa G (2015) Towards distributed region growing image segmentation based on MapReduce. In: 2015 IEEE International Geoscience and Remote Sensing Symposium, pp 4352–4355. https://doi.org/10.1109/IGARSS.2015.7326790

  23. He KM, Sun J (2015) Fast guide filter. IEEE Trans Pattern Anal Mach Intell 35(6):1397–1409

    Article  Google Scholar 

  24. Hua H, Wen J (2011) Remote sensing image segmentation based on region-split and graph cut. Key Eng Mater 474-476:15–18. https://doi.org/10.4028/www.scientific.net/KEM.474-476.15

    Article  Google Scholar 

  25. Huang HC, Chuang YY, Chen CS (2012) Multiple kernel fuzzy clustering. Fuzzy Syst 20(1):120–134. https://doi.org/10.1109/TFUZZ.2011.2170175

    Article  Google Scholar 

  26. Huang CW, Lin KP, Wu MC (2015) Intuitionistic fuzzy c-means clustering algorithm with neighborhood attraction in segmenting medical image. Pattern Anal Applic 18(1):459–470. https://doi.org/10.1007/s00500-014-1264-2

    Article  Google Scholar 

  27. James C, Bezdek (1976) A physical interpretation of fuzzy ISODATA. IEEE Trans Syst Man Cybern SMC-6(5):387–389. https://doi.org/10.1109/TSMC.1976.4309506

    Article  MathSciNet  MATH  Google Scholar 

  28. Ji J, Wang KL (2015) A robust nonlocal fuzzy clustering algorithm with between-cluster separation measure for SAR image segmentation. IEEE J Sel Top Appl Earth Obs Remote Sens 7(12):4929–4936. https://doi.org/10.1109/JSTARS.2014.2308531

    Article  Google Scholar 

  29. Ju ZW, Zhou JL, Wang X (2013) Image segmentation based on adaptive threshold edge detection and mean shift. IEEE 2013 IEEE 4th international conference on software engineering and service science (ICSESS), pp 385–388. https://doi.org/10.1109/ICSESS.2013.6615330

  30. Kanaani S, Helfroush MS, Danyali H. (2017) Segmentation of skin lesions using an improved FLICM method. International conference on computer and knowledge engineering (ICCKE), pp 445–449. https://doi.org/10.1109/ICCKE.2017.8167919

  31. Kou F, Chen WH, Wen CY (2015) Gradient domain guide image filtering. IEEE Trans Image Process 24(11):4528–4539. https://doi.org/10.1109/TIP.2015.2468183

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  33. Lei T, Jia X, Zhang Y (2018) Significantly fast and robust fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering. Fuzzy Syst 26(5):3027–3041. https://doi.org/10.1109/TFUZZ.2018.2796074

    Article  Google Scholar 

  34. Li ZG, Zheng JH, Zhu ZJ (2014) Weighted guide image filtering. IEEE Trans Image Process 24(1):120–129. https://doi.org/10.1109/TIP.2014.2371234

    Article  MATH  Google Scholar 

  35. Martin D, Fowlkes C, Tal D. (2002) A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. International Conference on Computer Vision (ICCV), 2, pp 416–423. https://doi.org/10.1109/ICCV.2001.937655

  36. Memon KH, Lee DH (2018) Generalised kernel weighted fuzzy c-means clustering algorithm with local information. Fuzzy Sets Syst 340:91–108. https://doi.org/10.1016/j.fss.2018.01.019

    Article  MathSciNet  MATH  Google Scholar 

  37. Oda H, Roth HR, Chiba K (2018) BESNet: boundary-enhanced segmentation of cells in histopathological images. International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 228–236. https://doi.org/10.1007/978-3-030-00934-2_26

  38. Panagiotakis C, Argyors A (2020) Region-based fitting of overlapping ellipses and its application to cells segmentation. Image Vis Comput 93:103810. https://doi.org/10.1016/j.imavis.2019.09.001

    Article  Google Scholar 

  39. Panigrahi L, Verma K, Singh BK (2018) Hybrid segmentation method based on multiscale Gaussian kernel fuzzy clustering with spatial bias correction and region scalable fitting for breast US images. IET Comput Vis 12(8):1067–1077. https://doi.org/10.1049/iet-cvi.2018.5332

    Article  Google Scholar 

  40. Patil AB, Shaikh JA (2016) OTSU thresholding method for flower image segmentation. Int J Comput Eng Res 6(5):1–6

    Google Scholar 

  41. Pham DL, Xu CY, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2:315–337. https://doi.org/10.1146/annurev.bioeng.2.1.315

    Article  Google Scholar 

  42. Qin GF, Li QT (2019) Pavement image segmentation based on fast FCM clustering with spatial information in internet of things. Multimedia Tools Appl 78(3):5181–5191. https://doi.org/10.1007/s11042-017-4683-0

    Article  Google Scholar 

  43. Rajaby E, Ahadi SM, Aghaeinia H (2016) Robust color image segmentation using fuzzy c-means with weighted hue and intensity. Digit Signal Process 51:170–183. https://doi.org/10.1016/j.dsp.2016.01.010

    Article  Google Scholar 

  44. Sing JK, Adhikari SK, Basu DK (2015) A modified fuzzy c-means algorithm using scale control spatial information for MRI image segmentation in the presence of noise. J Chemom 29(9):492–505. https://doi.org/10.1002/cem.2728

    Article  Google Scholar 

  45. Song JH, Cong W, Li JY (2017) A fuzzy C-means clustering algorithm for image segmentation using nonlinear weighted local information. J Inf Hiding Multimed Signal Process 8(3):578–588

    Google Scholar 

  46. Sui X, Chu SC, Pan JS (2020) Parallel compact differential evolution for optimization applied to image segmentation. Appl Sci 10(6):2195. https://doi.org/10.3390/app10062195

    Article  Google Scholar 

  47. Szilagyi L, Benyo Z, Szilagyi SM (2003) MR brain image segmentation using an enhanced fuzzy C-means algorithm. Proceedings of the 25th annual international conference of the IEEE engineering in medicine and biology society, 1, pp 724–726. https://doi.org/10.1109/IEMBS.2003.1279866

  48. Wang WN, Zhang YJ (2007) On fuzzy cluster validity indices. Fuzzy Sets Syst 158(19):2095–2117. https://doi.org/10.1016/j.fss.2007.03.004

    Article  MathSciNet  MATH  Google Scholar 

  49. Wang AN, Zhao Y, Hou YT. (2010) A novel construction of SVM compound kernel function. International conference on logistics systems and intelligent management (ICLSIM), pp 1462–1465. https://doi.org/10.1109/ICLSIM.2010.5461210

  50. Wu QP, Wu CM (2018) Fast robust kernel-based fuzzy c-means clustering segmentation. J Image Graph 23(12):1838–1851

    Google Scholar 

  51. Wu XC, Xia LB, Wu L (2010) Service-based global spatial data directory in spatial information grid. The 18th International Conference on Geoinformatics, pp 1–4. https://doi.org/10.1109/GEOINFORMATICS.2010.5567589

  52. Xie Y, Li L, Wang H (2010) The application of threshold methods for image segmentation in oasis vegetation extraction. International Conference on Geoinformatics, pp 18–20. https://doi.org/10.1109/GEOINFORMATICS.2010.5567552

  53. Xu GM, Dong JW, Zhou J (2018) An improved fuzzy c-means clustering algorithm with guided filter for image segmentation. 2018 international conference on security, pattern analysis, and cybernetics (SPAC), pp 255–258. https://doi.org/10.1109/SPAC46244.2018.8965448

  54. Xu GM, Zhou J, Dong JW (2020) Multivariate morphological reconstruction based fuzzy clustering with a weighting multi-channel guide image filter for color image segmentation. Int J Mach Learn Cybern 11(3):2793–2806. https://doi.org/10.1007/s13042-020-01151-1

    Article  Google Scholar 

  55. Yan Q, Shen XY, Li X (2013) Cross-field joint image restoration via scale map. IEEE international conference on computer vision, pp 1537–1544. https://doi.org/10.1109/ICCV.2013.194

  56. Yang B, Bu YY (2009) Multiple kernel LSSVM in empirical kernel mapping space. 2009 IITA international conference on control, automation and systems engineering, pp 636–639. https://doi.org/10.1109/case.2009.106

  57. Zhang XL (2019) Guided kernel fuzzy c-means clustering with spatial information for remote-sensing image segmentation. 2019 2nd international conference on intelligent systems research and mechatronics engineering (ISRME 2019), pp 253–259. https://doi.org/10.25236/isrme.2019.045

  58. Zhao F (2013) Fuzzy clustering algorithms with self-tuning non-local spatial information for image segmentation. Neurocomputing 106:115–125. https://doi.org/10.1016/j.neucom.2012.10.022

    Article  Google Scholar 

  59. Zhu HM, Guo YQ, Niu MW (2016) SAR image change detection based on Spark-FLICM algorithm. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp 3354–3357. https://doi.org/10.1109/IGARSS.2016.7729867

  60. Zhu HM, Lu L, Fan YC. (2016) Parallel implementation of the FLICM algorithm for SAR image change detection on intel MIC. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp 2340–2343. https://doi.org/10.1109/IGARSS.2016.7729604

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Authors and Affiliations

  1. School of Communication and Information Engineer, Xi’an University of Post &Telecommunications, Xi’an, 710100, China

    CaiCai Qiao & JiaYe Wang

  2. School of Electronic and Engineering, Xi’an University of Post & Telecommunications, Xi’an, 710100, China

    ChengMao Wu

  3. School of Science, Xi’an University of Post & Telecommunications, Xi’an, 710100, China

    ChangXing Li

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  1. CaiCai Qiao
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Qiao, C., Wu, C., Li, C. et al. Guided filter-driven kernel fuzzy clustering with local information for noise image segmentation. Multimed Tools Appl 81, 28431–28477 (2022). https://doi.org/10.1007/s11042-022-12840-0

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