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

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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.

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Data Availability

The data analyzed during the current study are available from the corresponding author upon request.

Code Availability

The code used in this work will be publicly shared once the paper is accepted for publication.

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Acknowledgements

The authors thank the editor, the associate editor, and the referees for their constructive comments that helped to improve this manuscript.

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Authors and Affiliations

  1. Université de Sousse, Ecole Nationale d’Ingénieurs de Sousse, LATIS-Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023, Tunisia

    Karim Kalti & Asma Touil

  2. Computer Science Department, Faculty of Science, University of Monastir, Monastir, 5019, Tunisia

    Karim Kalti

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  1. Karim Kalti
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  2. Asma Touil
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Correspondence to Karim Kalti.

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Kalti, K., Touil, A. A Robust Contextual Fuzzy C-Means Clustering Algorithm for Noisy Image Segmentation. J Classif 40, 488–512 (2023). https://doi.org/10.1007/s00357-023-09443-1

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