Noise-Estimation-Based Fuzzy C-Means Clustering for Image Segmentation

Cong Wang, Mengchu Zhou, Witold Pedrycz, Zhiwu Li

Research output: Chapter in Book/Report/Conference proceedingChapter


Since a noisy image has inferior characteristics, its direct use in Fuzzy C-Means (FCM) often produces poor image segmentation results. Intuitively, using its ideal value (noise-free image) benefits FCM's robustness enhancement. Therefore, the realization of accurate noise estimation in FCM is a new and important task. In this chapter, we elaborate residual-driven Fuzzy C-Means (FCM) for image segmentation, which is the first approach that realizes accurate residual (noise/outliers) estimation and makes noise-free image 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 noises. 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 noises. In addition, we make a comparative study of residual-driven FCM and only existing noise-estimation-based FCM, i.e., deviation-sparse FCM. Finally, 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)
Title of host publicationIntelligent Image and Video Analytics
Subtitle of host publicationClustering and Classification Applications
PublisherCRC Press
Number of pages52
ISBN (Electronic)9781000851908
ISBN (Print)9780367512989
StatePublished - Jan 1 2023

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Engineering


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