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Switching-based clustering algorithms for segmentation of low-level salt-and-pepper noise–corrupted images

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Abstract

This paper presents new clustering-based segmentation algorithms. The proposed switching-based clustering algorithms can minimize salt-and-pepper noise during segmentation without degrading the images’ fine details. The proposed algorithms incorporate the salt-and-pepper noise detection stage into the clustering algorithm, producing an adaptive technique specifically for segmentation of noisy images. Experimental results show that the proposed switching-based clustering algorithms produce better segmentation with fewer noise effects than conventional clustering algorithms. Quantitative and qualitative analyses show positive results for the proposed switching-based clustering algorithms, which consistently outperform conventional clustering algorithms in segmenting up to 50 % of salt-and-pepper noise density. Thus, these switching-based clustering algorithms can be used as pre- or post-processing task (i.e., segmenting images into regions of interest) in electronic products such as televisions and monitors.

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Acknowledgments

This work was partially supported by Universiti Sains Malaysia (USM) under the Research University grant ‘Study on Capability of FTIR Spectral Characteristics for Development of Intelligent Cervical Pre-cancerous diagnostic System.’

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Authors and Affiliations

  1. Imaging and Intelligent System Research Team (ISRT), School of Electrical and Electronics Engineering, Universiti Sains Malaysia, Engineering Campus, 14300 , Nibong Tebal, Penang, Malaysia

    Siti Noraini Sulaiman, Nor Ashidi Mat Isa & Intan Aidha Yusoff

  2. Faculty of Electrical Engineering, Universiti Teknologi MARA (UiTM), 13500, Permatang Pauh, Penang, Malaysia

    Siti Noraini Sulaiman & Fadzil Ahmad

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  1. Siti Noraini Sulaiman
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  2. Nor Ashidi Mat Isa
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  3. Intan Aidha Yusoff
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  4. Fadzil Ahmad
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Correspondence to Siti Noraini Sulaiman.

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Sulaiman, S.N., Mat Isa, N.A., Yusoff, I.A. et al. Switching-based clustering algorithms for segmentation of low-level salt-and-pepper noise–corrupted images. SIViP 9, 387–398 (2015). https://doi.org/10.1007/s11760-013-0455-0

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  • DOI: https://doi.org/10.1007/s11760-013-0455-0

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