Robust fuzzy clustering-based image segmentation

Zhang Yang, Fu Lai Korris Chung, Wang Shitong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

98 Citations (Scopus)


The fuzzy clustering algorithm fuzzy c-means (FCM) is often used for image segmentation. When noisy image segmentation is required, FCM should be modified such that it can be less sensitive to noise in an image. In this correspondence, a robust fuzzy clustering-based segmentation method for noisy images is developed. The contribution of the study here is twofold: (1) we derive a robust modified FCM in the sense of a novel objective function. The proposed modified FCM here is proved to be equivalent to the modified FCM given by Hoppner and Klawonn [F. Hoppner, F. Klawonn, Improved fuzzy partitions for fuzzy regression models, Int. J. Approx. Reason. 32 (2) (2003) 85-102]. (2) We explore the very applicability of the proposed modified FCM for noisy image segmentation. Our experimental results indicate that the proposed modified FCM here is very suitable for noisy image segmentation.
Original languageEnglish
Pages (from-to)80-84
Number of pages5
JournalApplied Soft Computing Journal
Issue number1
Publication statusPublished - 1 Jan 2009


  • Fuzzy clustering
  • Image segmentation
  • The objective function

ASJC Scopus subject areas

  • Software


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