Multimedia Tools and Applications

, Volume 76, Issue 6, pp 7869–7895 | Cite as

Improved fuzzy clustering algorithm with non-local information for image segmentation

  • Xiaofeng ZhangEmail author
  • Yujuan Sun
  • Gang Wang
  • Qiang Guo
  • Caiming Zhang
  • Beijing Chen


Fuzzy C-means(FCM) has been adopted to perform image segmentation due to its simplicity and efficiency. Nevertheless it is sensitive to noise and other image artifacts because of not considering spatial information. Up to now, a series of improved FCM algorithms have been proposed, including fuzzy local information C-means clustering algorithm(FLICM). In FLICM, one fuzzy factor is introduced as a fuzzy local similarity measure, which can control the trade-off between noise and details. However, the fuzzy factor in FLICM cannot estimate the damping extent of neighboring pixels accurately, which will result in poor performance in images of high-level noise. Aiming at solving this problem, this paper proposes an improved fuzzy clustering algorithm, which introduces pixel relevance into the fuzzy factor and could estimate the damping extent accurately. As a result, non-local context information can be utilized in the improved algorithm, which can improve the performance in restraining image artifacts. Experimental results on synthetic, medical and natural images show that the proposed algorithm performs better than current improved algorithms.


Fuzzy clustering Image segmentation FLICM Pixel relevance Non-local information 



The authors would like to thank anonymous referees for their valuable comments and suggestions which lead to substantial improvements of this paper. Also, the authors would like to thank Dr. Krindis and Dr. Maoguo GONG for providing the source codes and experimental pictures of FLICM and KWFLICM. We would also thank Dr. Weiling CAI for providing the codes of FCMS1, FCMS2 and FGFCM. The research was supported by NSF of China (61232016, U1405254, 61373078, 61472220, 61502218), the Priority Academic Program Development of Jiangsu Higer Education Institutions, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, NSF of Shandong Province(ZR2014FM005), Key Technology Research and Development Program of Shandong Province(2015GSF116001,2015GGX101004), Shandong Province Higher Educational Science and Technology Program(J14LN20), and Doctoral Foundation of Ludong University(LY2015035).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Xiaofeng Zhang
    • 1
    • 3
    Email author
  • Yujuan Sun
    • 1
  • Gang Wang
    • 1
  • Qiang Guo
    • 2
    • 3
  • Caiming Zhang
    • 2
    • 3
  • Beijing Chen
    • 4
  1. 1.School of Information and Electrical EngineeringLudong UniversityYantaiChina
  2. 2.School of Computer Science and TechnologyShandong University of Finance and EconomicsJinanChina
  3. 3.Shandong Provincial Key Laboratory of Digital Media TechnologyJinanChina
  4. 4.School of Computer & SoftwareNanjing University of Information Science & TechnologyNanjingChina

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