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 improved GLA (Ge...
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Series: | Computational and Mathematical Methods in Medicine |
Online Access: | http://dx.doi.org/10.1155/2017/8064743 |
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doaj-15241ad5e5af4293a125572fa3d25dbc2020-11-24T20:52:16ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182017-01-01201710.1155/2017/80647438064743Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in RetrievalLiyan Chen0Beizhan Wang1Zhihong Zhang2Fan Lin3Yihan Ma4Software School of Xiamen University, Xiamen 361005, ChinaSoftware School of Xiamen University, Xiamen 361005, ChinaSoftware School of Xiamen University, Xiamen 361005, ChinaSoftware School of Xiamen University, Xiamen 361005, ChinaSoftware School of Xiamen University, Xiamen 361005, ChinaTongue 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.http://dx.doi.org/10.1155/2017/8064743 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Liyan Chen Beizhan Wang Zhihong Zhang Fan Lin Yihan Ma |
spellingShingle |
Liyan Chen Beizhan Wang Zhihong Zhang Fan Lin Yihan Ma Research on Techniques of Multifeatures Extraction for Tongue Image and Its Application in Retrieval Computational and Mathematical Methods in Medicine |
author_facet |
Liyan Chen Beizhan Wang Zhihong Zhang Fan Lin Yihan Ma |
author_sort |
Liyan Chen |
title |
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_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_sort |
research on techniques of multifeatures extraction for tongue image and its application in retrieval |
publisher |
Hindawi Limited |
series |
Computational and Mathematical Methods in Medicine |
issn |
1748-670X 1748-6718 |
publishDate |
2017-01-01 |
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. |
url |
http://dx.doi.org/10.1155/2017/8064743 |
work_keys_str_mv |
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1716800336297984000 |