A Robust Contextual Fuzzy C-Means Clustering Algorithm for Noisy Image Segmentation

Karim Kalti () and Asma Touil ()
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Karim Kalti: Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems
Asma Touil: Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems

Journal of Classification, 2023, vol. 40, issue 3, No 3, 488-512

Abstract: Abstract In this paper, we address the problem of the fuzzy c-means (FCM) algorithm sensitivity to noise when clustering image pixels. We propose in this regard an improved FCM algorithm that incorporates contextual information at the membership degrees updating stage. For that aim, we introduce two novel parameters: the contextual similarity degree and the intrinsic similarity degree which are used to estimate each pixel’s nature (normal or noisy), according respectively to its context and to its specific features. Based on this estimation, we propose a modified membership degrees updating strategy that proceeds by adaptively reinforcing the assignment of a pixel to its context’s cluster when this pixel is detected as noisy. Experiments performed on synthetic and real-world images proved that our approach achieves competitive performance compared to state-of-the-art FCM-based methods.

Keywords: Fuzzy c-means (FCM); Contextual clustering; Spatial information; Noise reduction (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00357-023-09443-1

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