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

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 acc...

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Detalles Bibliográficos
Autores principales: Yang, Wei, Cai, Lulu, Wu, Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7053740/
https://www.ncbi.nlm.nih.gov/pubmed/32126113
http://dx.doi.org/10.1371/journal.pone.0229651
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author Yang, Wei
Cai, Lulu
Wu, Fei
author_facet Yang, Wei
Cai, Lulu
Wu, Fei
author_sort Yang, Wei
collection PubMed
description 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.
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spelling pubmed-70537402020-03-12 Image segmentation based on gray level and local relative entropy two dimensional histogram Yang, Wei Cai, Lulu Wu, Fei PLoS One Research Article 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. Public Library of Science 2020-03-03 /pmc/articles/PMC7053740/ /pubmed/32126113 http://dx.doi.org/10.1371/journal.pone.0229651 Text en © 2020 Yang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Yang, Wei
Cai, Lulu
Wu, Fei
Image segmentation based on gray level and local relative entropy two dimensional histogram
title Image segmentation based on gray level and local relative entropy two dimensional histogram
title_full Image segmentation based on gray level and local relative entropy two dimensional histogram
title_fullStr Image segmentation based on gray level and local relative entropy two dimensional histogram
title_full_unstemmed Image segmentation based on gray level and local relative entropy two dimensional histogram
title_short Image segmentation based on gray level and local relative entropy two dimensional histogram
title_sort image segmentation based on gray level and local relative entropy two dimensional histogram
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7053740/
https://www.ncbi.nlm.nih.gov/pubmed/32126113
http://dx.doi.org/10.1371/journal.pone.0229651
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