Abstract
Fuzzy clustering algorithm is a frequently used method for image segmentation, which allows pixel to be classified into one or more clusters with respect to its membership level. However, its segmentation performance often suffered from the factors associated with the drift of cluster centers and the sensitiveness to the intensity overlap of distribution between classes. In this paper, we solve these drawbacks and present a modified strategy of fuzzy clustering algorithm for image segmentation. This strategy generally consists of two-pass processes. The first process is to directly calculate the cluster centers from the segmented image and then take the higher value of cluster centers as an alternative threshold to prevent the pixels with lower intensity from clustering. The second process thereby makes use of the fuzzy clustering algorithm with a bias field for partitioning pixels with spatial proximity, ensuring that our method is less sensitive to the drawbacks inherent in the fuzzy clustering algorithm and thus obtaining promising results. Experiments on synthetic and some representative infrared images demonstrate that the proposed method outperforms fuzzy c-means methods and its existing variants in terms of segmentation performance, and is less sensitive to the intensity overlap of the distribution between classes.
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References
Ahmed MN, Yamany SM, Mohamed N et al (2002) A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21(3):193–199
Balafar MA (2014) Fuzzy c-means based brain MRI segmentation algorithms. Artif Intell Rev 41(3):441–449
Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithm. Plenum Press, New York
Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit 40(3):825–838
Chen S, Zhang D (2004) Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure. IEEE Trans Syst Man Cybern 34(4):1907–1916
Dante MV, Francisco JGF, Alberto JRS et al (2011) Robust RML estimator-fuzzy c-means clustering algorithms for noisy image segmentation. Lect Notes Comput Sci 7095:474–486
Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3(3):32–57
Feng J, Jiao LC, Zhang X et al (2013) Robust non-local fuzzy c-means algorithm with edge preservation for SAR image segmentation. Signal Process 93(2):487–499
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
Goubet E, Katz J, Porikli F (2006) Pedestrian tracking using thermal infrared imaging. In: Proceedings of the SPIE, Kissimmee, p 62062C-1-12
Ji ZX, Liu JY, Cao G, Sun QS (2014) Robust spatial constrained fuzzy c-means algorithm for brain MR image segmentation. Pattern Recognit 47(7):2454–2466
Ji ZX, Sun QS, Xia DS (2011) A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image. Comput Med Imaging Graph 35(5):383–397
Kang J, Min L, Luan Q et al (2009) Novel modified fuzzy c-means algorithm with applications. Digit Signal Process 19(2):309–319
Krinidis S, Chatzis V (2010) A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337
Krinidis S, Krinidis M (2012) Generalized fuzzy local information c-means clustering algorithm. Electron Lett 48(23):1468–1470
Lei J, Yang W (2003) A modified fuzzy c-means algorithm for segmentation of magnetic resonance images. In: Proceedings of VII-th digital image computing: techniques and applications, Sydney, pp 225–231
Li YL, Shen Y (2010) An automatic fuzzy c-means algorithm for image segmentation. Soft Comput 14:123–128
Liu J, Xu M (2008) Kernelized fuzzy attribute c-means clustering algorithm. Fuzzy Sets Syst 159(18):2428–2445
Ma L, Staunton RC (2007) A modified fuzzy c-means image segmentation algorithm for use with uneven illumination patterns. Pattern Recognit 40(11):3005–3011
Mujica-Vargas D, Gallegos-Funes FJ, Rosales-Silva AJ (2013) A fuzzy clustering algorithm with spatial robust estimation constraint for noisy color image segmentation. Pattern Recognit Lett 34(4):400–413
Pham DL, Princea JL (1999) An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognit Lett 20(1):57–68
Sezgin M, Sankur B (2004) Survey over image thresholding techniques and quantitative performance evaluation. J Electron Imaging 13(1):146–165
Sikka K, Sinha N, Singh PK et al (2009) A fully automated algorithm under modified FCM framework for improved brain MR image segmentation. Magn Reson Imaging 27(7):994–1004
Szilagyi L, Benyo Z, Szilagyi SM et al (2003) MR brain image segmentation using an enhanced fuzzy c-means algorithm. In: 25th Annual international conference of IEEE engineering in medicine and biology, Cancun, Mexico, pp 17–21
Szilagyi L, Szilzgyi SM, Benyo Z (2007) A modified FCM algorithm for fast segmentation of brain MR images. Adv Soft Comput 41:119–127
Wang H, Fei B (2009) A modified fuzzy c-means classification method using a multiscale diffusion filtering scheme. Med Image Anal 13(2):193–202
Wang J, Kong J, Lu Y et al (2008) A modified FCM algorithm for MRI brain image segmentation using both local and non-local spatial constraints. Comput Med Imaging Graph 32(8):685–698
Wang ZM, Song Q, Soh YC, Sim K (2013) An adaptive spatial information-theoretic fuzzy clustering algorithm for image segmentation. Comput Vis Image Underst 117(10):1412–1420
Yang MS, Tsai HS (2008) A Gaussian kernel-based fuzzy c-means algorithm with a spatial bias correction. Pattern Recognit Lett 29(12):1713–1725
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Zhang D, Chen S (2004) A novel kernelized fuzzy c-means algorithm with application in medical image segmentation. Artif Intell Med 32(1):37–50
Zhang S, She LH, Lu L (2013) A modified fuzzy c-means for bias field estimation and segmentation of brain MR image. In: Proceedings of 25th Chinese control and decision conference, Guiyang, China, pp 2080–2085
Zhao F (2013) Fuzzy clustering algorithms with self-tuning non-local spatial information for image segmentation. Neurocomputing 106:115–125
Zhao F, Jiao L, Liu H (2013) Kernel generalized fuzzy c-means clustering with spatial information for image segmentation. Digit Signal Process 23(1):184–199
Zhao F, Jiao L, Liu H et al (2011a) A novel fuzzy clustering algorithm with non local adaptive spatial constraint for image segmentation. Signal Process 91(4):988–999
Zhao F, Jiao L (2011b) Spatial improved fuzzy c-means clustering for image segmentation. International conference on electronic and mechanical engineering and information technology, Harbin, Heilongjiang, China, pp 4791–4794
Zhou H, Schaefer G (2009) An overview of fuzzy c-means based image clustering algorithms. Foundations of computational intelligence, vol 2. Springer, Berlin, Heidelberg, pp 295–310
Zhu L, Chung F, Wang S (2009) Generalized fuzzy c-means clustering algorithm with improved fuzzy partitions. IEEE Trans Syst Man Cybern Part B Cybern 39(3):578–591
Acknowledgments
The authors would like to thank Professor Chao Gao, Chongqing University, for providing the experimental images and all my colleagues in the laboratory for very helpful comments and suggestions.
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Communicated by V. Loia.
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Zhou, D., Zhou, H. A modified strategy of fuzzy clustering algorithm for image segmentation. Soft Comput 19, 3261–3272 (2015). https://doi.org/10.1007/s00500-014-1481-8
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DOI: https://doi.org/10.1007/s00500-014-1481-8