Soft Computing

, Volume 21, Issue 8, pp 2165–2173 | Cite as

An improved fuzzy algorithm for image segmentation using peak detection, spatial information and reallocation

  • Xiaofeng Zhang
  • Gang Wang
  • Qingtang Su
  • Qiang Guo
  • Caiming Zhang
  • Beijing Chen
Methodologies and Application

Abstract

Image segmentation is a crucial step in image processing, especially for medical images. However, the existence of partial volume effect, noise and other artifacts makes this problem much more complex. Fuzzy c-means (FCM), as an effective tool to deal with partial volume effect, cannot deal with noise and other artifacts. In this paper, one modified FCM algorithm is proposed to solve the above problems, which includes three main steps: (1) peak detection is used to initialize cluster centers, which can make the initial centers close to the final ones and in turn decrease the number of iterations; (2) fuzzy clustering incorporating spatial information is implemented, which can make the algorithm robust to image artifacts; (3) the segmentation results are refined further by detecting and reallocating the misclassified pixels. Experiments are performed on both synthetic and medical images, and the results show that our proposed algorithm is more effective and reliable than other FCM-based algorithms.

Keywords

Image segmentation FCM Peak detection Spatial information reallocation 

References

  1. Ahmed MN, Yamany SM, Mohamed N, Farag AA, Moriarty T (2002) A modified fuzzy c-mean algorithm for bias field estimation and segmentation of MRI data. IEEE Trans Med Imaging 21(3):193–199CrossRefGoogle Scholar
  2. Benaichouche AN, Oulhadj H, Siarry P (2013) Improved spatial fuzzy c-means clustering for image segmentation using PSO initialization, Mahalanobis distance and post-segmentation correction. Digit Signal Process 23(5):1390–1400. doi:10.1016/j.dsp.2013.07.005 MathSciNetCrossRefGoogle Scholar
  3. Bezdek JC (1974) Cluster validity with fuzzy sets. J Cybern 3(3):58–73MathSciNetCrossRefMATHGoogle Scholar
  4. Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Kluwer Academic Publishers, NorwellCrossRefMATHGoogle Scholar
  5. Cocosco C, Kollokian V, Kwan RKS, Pike GB, Evans A (1997) Brainweb: online interface to a 3D MRI simulated brain database. Neuroimage 5(4):S425Google Scholar
  6. Cai W, Chen S, Zhang D (2007) Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation. Pattern Recognit 40:825–838CrossRefMATHGoogle Scholar
  7. 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 Part B Cybern 34:1907–1916CrossRefGoogle Scholar
  8. Chuang KS, Tzeng HL, Chen S, Wu J, Chen TJ (2006) Fuzzy c-means clustering with spatial information for image segmentation. Comput Med Imaging Graph 30:9–15CrossRefGoogle Scholar
  9. Elmazi D, Kulla E, Matsuo K, Oda T, Spaho E, Barolli L (2015) A mobility-aware fuzzy-based system for actor selection in wireless sensor–actor networks. J High Speed Netw 21(1):15–25CrossRefGoogle Scholar
  10. Graves D, Pedrycz W (2010) Kernel-based fuzzy clustering and fuzzy clustering: a comparative experimental study. Fuzzy Sets Syst 161:522–543MathSciNetCrossRefGoogle Scholar
  11. Guo Q, Zhang C, Zhang Y, Liu H (2015) An efficient SVD-based method for image denoising. IEEE Trans Circuits Syst Video Technol. doi:10.1109/TCSVT.2015.2416631
  12. Ji Z, Sun Q, Xia D (2010) A modified possibilistic fuzzy c-means clustering algorithm for bias field estimation and segmentation of brain MR image. Comput Med Imaging Graph 35:383–397CrossRefGoogle Scholar
  13. Ji Z, Sun Q, Xia D (2011) A framework with modified fast FCM for brain MR images segmentation. Pattern Recognit 44:999–1013CrossRefGoogle Scholar
  14. Kannan SR, Ramathilagam S, Devi R, Sathya A (2011) Robust kernel FCM in segmentation of breast medical images. Expert Syst Appl 38:4382–4389CrossRefGoogle Scholar
  15. Krinidis S, Chatzis V (2010) A robust fuzzy local information c-means clustering algorithm. IEEE Trans Image Process 19(5):1328–1337MathSciNetCrossRefGoogle Scholar
  16. Li J, Li X, Yang B, Sun X (2015a) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518CrossRefGoogle Scholar
  17. Li J, Xhafa F, Weng J (2015b) Emerging services and technologies in wireless networks. J High Speed Netw 21(2):81–82CrossRefGoogle Scholar
  18. Liu Y, Mu C, Kou W, Liu J (2015) Modified particle swarm optimization-based multilevel thresholding for image segmentation. Soft Comput 19(5):1311–1327CrossRefGoogle Scholar
  19. Liu J, Yang YH (1994) Multiresolution color image segmentation. IEEE Trans Pattern Anal Mach Intell 16(7):689–700. doi:10.1109/34.297949 CrossRefGoogle Scholar
  20. Noordam JC, van den Broek WHAM, Buydens LMC (2000) Geometrically guided fuzzy c-means clustering for multivariate image segmentation. In: Proceedings of International Conferene on Pattern Reognition, vol 1, pp 462–465Google Scholar
  21. Pedrycz W (2005) Knowledge-based clustering: from data to information granules. Wiley, HobokenCrossRefMATHGoogle Scholar
  22. Pham DL, Xu C, Prince JL (2000) A survey of concurrent methods in medical image segmentation. Annu Rev Biomed Eng 2:315–337CrossRefGoogle Scholar
  23. Szilágyi L, Benyó Z, Szilágyii SM (2007) A modified fuzzy c-means algorithm for MR brain image segmentation. Image Anal Recognit 4633:866–877Google Scholar
  24. Tolias YA, Panas SM (1998) Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions. IEEE Trans Syst Man Cybern Part B Cybern 28(3):359–369CrossRefGoogle Scholar
  25. Wang G, Zhang X, Su Q, Shi J, Caselli RJ, Wang Y (2015) A novel cortical thickness estimation method based on volumetric laplace—beltrami operator and heat kernel. Med Image Anal 22:1–20. doi:10.1016/j.media.2015.01.005 CrossRefGoogle Scholar
  26. Xie XL, Beni GA (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13(8):841–847. doi:10.1109/34.85677 CrossRefGoogle Scholar
  27. Yang MS, Hu YJ, Lin KCR, Lin CCL (2002) Segmentation techniques for tissue differentiation in MRI of ophthalmology using fuzzy clustering algorithms. Magn Reson Imaging 20(2):173–179CrossRefGoogle Scholar
  28. Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353CrossRefMATHGoogle Scholar
  29. Zhang X, Zhang C, Tang W, Wei Z (2012a) Medical image segmentation using improved FCM. Sci China Inf Sci 55(4):1052–1061MathSciNetCrossRefGoogle Scholar
  30. Zhang X, Zhang C, Zou H, Zhang C (2012b) One improved FCM for image segmentation based on pixel relevance. Adv Sci Lett 10(1):539–543CrossRefGoogle Scholar
  31. Zheng Y, Jeon B, Xu D, Jonathan WQM, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy c-means algorithm. J Intell Fuzzy Syst 28(2):961–973Google Scholar
  32. Zheng F, Zhang C, Zhang X, Liu Y (2013) A fast anti-noise fuzzy c-means algorithm for image segmentation. In: Proceedings of ICIP 2013, pp 2728–2732Google Scholar
  33. Zhou L, He Y, Chen H, Liu J (2014) A fuzzy mathematical morphology based on discrete t-norms: fundamentals and applications to image processing. Soft Comput 18(11):2297–2311CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Xiaofeng Zhang
    • 1
  • Gang Wang
    • 1
  • Qingtang Su
    • 1
  • Qiang Guo
    • 2
  • Caiming Zhang
    • 2
  • Beijing Chen
    • 3
  1. 1.School of Information and Electrical EngineeringLudong UniversityYantaiChina
  2. 2.School of Computer Science and TechnologyShandong UniversityJinanChina
  3. 3.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingChina

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