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A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering

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Abstract

This paper presents a novel image segmentation algorithm based on neutrosophic c-means clustering and indeterminacy filtering method. Firstly, the image is transformed into neutrosophic set domain. Then, a new filter, indeterminacy filter is designed according to the indeterminacy value on the neutrosophic image, and the neighborhood information is utilized to remove the indeterminacy in the spatial neighborhood. Neutrosophic c-means clustering is then used to cluster the pixels into different groups, which has advantages to describe the indeterminacy in the intensity. The indeterminacy filter is employed again to remove the indeterminacy in the intensity. Finally, the segmentation results are obtained according to the refined membership in the clustering after indeterminacy filtering operation. A variety of experiments are performed to evaluate the performance of the proposed method, and a newly published method neutrosophic similarity clustering (NSC) segmentation algorithm is utilized to compare with the proposed method quantitatively. The experimental results show that the proposed algorithm has better performances in quantitatively and qualitatively.

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Authors and Affiliations

  1. Department of Computer Science, University of Illinois at Springfield, Springfield, IL, USA

    Yanhui Guo

  2. Oracle, Westminster, CO, USA

    Rong Xia

  3. Department of Electrical and Electronics Engineering, Firat University, Elazig, Turkey

    Abdulkadir Şengür

  4. Department of Electrical and Electronics Engineering, Abant Izzet Baysal University, Bolu, Turkey

    Kemal Polat

Authors
  1. Yanhui Guo
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  2. Rong Xia
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  3. Abdulkadir Şengür
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  4. Kemal Polat
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Correspondence to Yanhui Guo.

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Guo, Y., Xia, R., Şengür, A. et al. A novel image segmentation approach based on neutrosophic c-means clustering and indeterminacy filtering. Neural Comput & Applic 28, 3009–3019 (2017). https://doi.org/10.1007/s00521-016-2441-2

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  • DOI: https://doi.org/10.1007/s00521-016-2441-2

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