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Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval

Tongue diagnosis is one of the important methods in the Chinese traditional medicine. Doctors can judge the disease's situation by observing patient's tongue color and texture. This paper presents a novel approach to extract color and texture features of tongue images. First, we use improv...

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Detalles Bibliográficos
Autores principales: Chen, Liyan, Wang, Beizhan, Zhang, Zhihong, Lin, Fan, Yihan Ma
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390548/
https://www.ncbi.nlm.nih.gov/pubmed/28465714
http://dx.doi.org/10.1155/2017/8064743
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author Chen, Liyan
Wang, Beizhan
Zhang, Zhihong
Lin, Fan
Yihan Ma,
author_facet Chen, Liyan
Wang, Beizhan
Zhang, Zhihong
Lin, Fan
Yihan Ma,
author_sort Chen, Liyan
collection PubMed
description Tongue diagnosis is one of the important methods in the Chinese traditional medicine. Doctors can judge the disease's situation by observing patient's tongue color and texture. This paper presents a novel approach to extract color and texture features of tongue images. First, we use improved GLA (Generalized Lloyd Algorithm) to extract the main color of tongue image. Considering that the color feature cannot fully express tongue image information, the paper analyzes tongue edge's texture features and proposes an algorithm to extract them. Then, we integrate the two features in retrieval by different weight. Experimental results show that the proposed method can improve the detection rate of lesion in tongue image relative to single feature retrieval.
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spelling pubmed-53905482017-05-02 Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval Chen, Liyan Wang, Beizhan Zhang, Zhihong Lin, Fan Yihan Ma, Comput Math Methods Med Research Article Tongue diagnosis is one of the important methods in the Chinese traditional medicine. Doctors can judge the disease's situation by observing patient's tongue color and texture. This paper presents a novel approach to extract color and texture features of tongue images. First, we use improved GLA (Generalized Lloyd Algorithm) to extract the main color of tongue image. Considering that the color feature cannot fully express tongue image information, the paper analyzes tongue edge's texture features and proposes an algorithm to extract them. Then, we integrate the two features in retrieval by different weight. Experimental results show that the proposed method can improve the detection rate of lesion in tongue image relative to single feature retrieval. Hindawi 2017 2017-03-30 /pmc/articles/PMC5390548/ /pubmed/28465714 http://dx.doi.org/10.1155/2017/8064743 Text en Copyright © 2017 Liyan Chen et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chen, Liyan
Wang, Beizhan
Zhang, Zhihong
Lin, Fan
Yihan Ma,
Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval
title Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval
title_full Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval
title_fullStr Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval
title_full_unstemmed Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval
title_short Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval
title_sort research on techniques of multifeatures extraction for tongue image and its application in retrieval
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5390548/
https://www.ncbi.nlm.nih.gov/pubmed/28465714
http://dx.doi.org/10.1155/2017/8064743
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