Residual-driven Fuzzy C-Means Clustering for Image Segmentation

Cong Wang, Witold Pedrycz, Zhi Wu Li, Meng Chu Zhou

Research output: Contribution to journalArticlepeer-review

65 Scopus citations


In this paper, we elaborate on residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual (noise/outliers) estimation and enables noise-free image to participate in clustering. We propose a residual-driven FCM framework by integrating into FCM a residual-related regularization term derived from the distribution characteristic of different types of noise. Built on this framework, a weighted ℓ2-norm regularization term is presented by weighting mixed noise distribution, thus resulting in a universal residual-driven FCM algorithm in presence of mixed or unknown noise. Besides, with the constraint of spatial information, the residual estimation becomes more reliable than that only considering an observed image itself. Supporting experiments on synthetic, medical, and real-world images are conducted. The results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.

Original languageEnglish (US)
Article number9242330
Pages (from-to)876-889
Number of pages14
JournalIEEE/CAA Journal of Automatica Sinica
Issue number4
StatePublished - Apr 2021

All Science Journal Classification (ASJC) codes

  • Control and Optimization
  • Artificial Intelligence
  • Information Systems
  • Control and Systems Engineering


  • Fuzzy C-Means
  • image segmentation
  • mixed or unknown noise
  • residual-driven
  • weighted regularization


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