Tongue Color Analysis for Medical Application

Zhang, Bob; Wang, Xingzheng; You, Jane; Zhang, David

doi:10.1155/2013/264742

Cited by 48 publications

(44 citation statements)

References 10 publications

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“…Along with the achievements obtained in the digitization and objectification of tongue image diagnosis in TCM, many other researches about prediction of health state or abnormity of physical indicators in western medicine have been done recently [20, 22–25, 28, 34, 35]. Though they have successfully set up certain connection between the objective tongue feature parameter and the health state, it is difficult for them to be accepted and admitted by the TCM clinicians for their abstraction in the tongue diagnosis.…”

Section: Discussionmentioning

confidence: 99%

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The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

et al. 2016

BioMed Research International

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Background and Goal. The application of digital image processing techniques and machine learning methods in tongue image classification in Traditional Chinese Medicine (TCM) has been widely studied nowadays. However, it is difficult for the outcomes to generalize because of lack of color reproducibility and image standardization. Our study aims at the exploration of tongue colors classification with a standardized tongue image acquisition process and color correction. Methods. Three traditional Chinese medical experts are chosen to identify the selected tongue pictures taken by the TDA-1 tongue imaging device in TIFF format through ICC profile correction. Then we compare the mean value of L * a * b * of different tongue colors and evaluate the effect of the tongue color classification by machine learning methods. Results. The L * a * b * values of the five tongue colors are statistically different. Random forest method has a better performance than SVM in classification. SMOTE algorithm can increase classification accuracy by solving the imbalance of the varied color samples. Conclusions. At the premise of standardized tongue acquisition and color reproduction, preliminary objectification of tongue color classification in Traditional Chinese Medicine (TCM) is feasible.

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Section: Discussionmentioning

confidence: 99%

“…Machine learning methods used in tongue image classification include KNN [20, 21], Naive Bayes [22], Decision Tree [22], SVM [20, 22–25], Neutral Network [26], Graphical Models [22, 27], and Principle Component Analysis (PCA) [28]. KNN and SVM are the most commonly used methods.…”

Section: Introductionmentioning

confidence: 99%

The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

et al. 2016

BioMed Research International

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“…In recent years, with ubiquitous application of advanced computer technologies in medical science, we cannot ignore new possibilities to improve TCM with computer technologies [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]; these possibilities for improvement have drawn attention from myriads of researchers. Chiu [4,5] built a computerized tongue examination system (CTES) based on computerized image analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Pang et al [6] used Bayesian networks to build tongue diagnosis model which provided a systematic and objective diagnostic standard for the tongue diagnosis. Zhang et al [7] proposed an in-depth systematic tongue color analysis system for medical applications. Using the tongue color gamut, tongue foreground pixels are first extracted and assigned to one of 12 colors representing this gamut.…”

Section: Introductionmentioning

confidence: 99%

WLDF: Effective Statistical Shape Feature for Cracked Tongue Recognition

Li¹,

Wang²,

Cui³

2017

Journal of Electrical Engineering and Technology

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-This paper proposes a new method using Wide Line Detector based statistical shape Feature (WLDF) to identify whether or not a tongue is cracked; a cracked tongue is one of the most frequently used visible features for diagnosis in traditional Chinese Medicine (TCM). We first detected a wide line in the tongue image, and then extracted WLDF, such as the maximum length of each detected region, and the ratio between maximum length and the area of the detected region. We trained a binary support vector machine (SVM) based on the WLDF to build a classifier for cracked tongues. We conducted an experiment based on our proposed scheme, using 196 samples of cracked tongues and 245 samples of non-cracked tongues. The results of the experiment indicate that the recognition accuracy of the proposed method is greater than 95%. In addition, we provide an analysis of the results of this experiment with different parameters, demonstrating the feasibility and effectiveness of the proposed scheme.

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“…The human tongue consists of various features that could be employed for disease diagnosis (Chang et al, 2014). Out of all the features, the properties with respect to the colour of the tongue are considered as the most striking component (Chiu, 2000;Zhang et al, 2013). Several medical practitioners typically used colour features as the prominent criteria for examination.…”

Section: Introductionmentioning

confidence: 99%

Virtual doctor: an artificial medical diagnostic system based on hard and soft inputs

Karthik¹,

Menaka

Kulkarni

et al. 2014

IJBET

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The objective of this research is to develop an automatic medical diagnostic system which can be readily available to the common man, especially to those who cannot receive proper medical care. The approach basically includes a combination of soft and hard inputs. Soft inputs include a variety of common symptoms such as fever, headache and cough. Each selected disease corresponds to a variety of general symptoms. Hard inputs include images of the tongue as it is widely used by the doctor to diagnose the various disorders. The analysis of hard inputs was divided into two phases, namely chromatic colour analysis and texture-based statistical analysis. Once the feature vectors are encoded from the hard and soft inputs, they are fed to a neural network for developing a classification model. Neural network is trained with four different algorithms and the performance is analysed.

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Tongue Color Analysis for Medical Application

Cited by 48 publications

References 10 publications

The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

WLDF: Effective Statistical Shape Feature for Cracked Tongue Recognition

Virtual doctor: an artificial medical diagnostic system based on hard and soft inputs

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