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Image segmentation based on gray level and local relative entropy two dimensional histogram

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  • Wei Yang
  • Lulu Cai
  • Fei Wu

Abstract

Though traditional thresholding methods are simple and efficient, they may result in poor segmentation results because only image’s brightness information is taken into account in the procedure of threshold selection. Considering the contextual information between pixels can improve segmentation accuracy. To to this, a new thresholding method is proposed in this paper. The proposed method constructs a new two dimensional histogram using brightness of a pixel and local relative entropy of it’s neighbor pixels. The local relative entropy (LRE) measures the brightness difference between a pixel and it’s neighbor pixels. The two dimensional histogram, consisting of gray level and LRE, can reflect the contextual information between pixels to a certain extent. The optimal thresholding vector is obtained via minimizing cross entropy criteria. Experimental results show that the proposed method can achieve more accurate segmentation results than other thresholding methods.

Suggested Citation

  • Wei Yang & Lulu Cai & Fei Wu, 2020. "Image segmentation based on gray level and local relative entropy two dimensional histogram," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-9, March.
    • Handle: RePEc:plo:pone00:0229651
    DOI: 10.1371/journal.pone.0229651
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    References listed on IDEAS

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    1. Schlittgen, Rainer & Ringle, Christian M. & Sarstedt, Marko & Becker, Jan-Michael, 2016. "Segmentation of PLS path models by iterative reweighted regressions," Journal of Business Research, Elsevier, vol. 69(10), pages 4583-4592.
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    Cited by:

    1. JianWu Long & ZeRan Yan & HongFa Chen & XinLei Song, 2021. "Spectrum decomposition in Gaussian scale space for uneven illumination image binarization," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-24, April.
    2. Steven B Kim & Dong Sub Kim & Xiaoming Mo, 2021. "An image segmentation technique with statistical strategies for pesticide efficacy assessment," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-12, March.
    3. Sadia Basar & Mushtaq Ali & Gilberto Ochoa-Ruiz & Mahdi Zareei & Abdul Waheed & Awais Adnan, 2020. "Unsupervised color image segmentation: A case of RGB histogram based K-means clustering initialization," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-21, October.

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