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Fully Integrated Spatial Information to Improve FCM Algorithm for Brain MRI Image Segmentation

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Abstract

FCM algorithm is one of the well-known techniques for image segmentation; it is based on an imprecise decision by using the membership function. However, FCM algorithm fails to proceed well enough in the presence of imaging artifacts due to its performance without any consideration of spatial information. In this paper, we propose two crucial modifications to the conventional FCM algorithm to tackle its sensitivity against noise. Firstly, the proposed algorithm provides full consideration of the spatial constraint, wherein the influence of neighboring pixels is defined according to two proposed terms, a fuzzy similarity measure as well as the level of noise. Secondly, we adopt a strategy to select the optimal pixel between the central pixel and its neighboring pixels that can better influence the segmentation performance in terms of compactness and separation information. The proposed algorithm is compared qualitatively and quantitatively with five existing clustering methods in terms of cluster validity functions, segmentation accuracy, tissue segmentation accuracy, and computational time.

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Authors and Affiliations

  1. Department of Computer Science, University of Biskra, 07000, Biskra, Algeria

    Fouzia Chighoub &  Rachida Saouli

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  1. Fouzia Chighoub
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  2. Rachida Saouli
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Correspondence to Fouzia Chighoub or Rachida Saouli.

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Fouzia Chighoub, Rachida Saouli Fully Integrated Spatial Information to Improve FCM Algorithm for Brain MRI Image Segmentation. Aut. Control Comp. Sci. 56, 67–82 (2022). https://doi.org/10.3103/S0146411622010047

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