Tongue's substance and coating recognition analysis using HSV color threshold in tongue diagnosis

Kamarudin, Nur Diyana; Ooi, Chia Yee; Kawanabe, Tadaaki; Mi, Xiaoyu

doi:10.1117/12.2242404

Cited by 11 publications

(13 citation statements)

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“…Fortunately, computational methods for digital image processing techniques in tongue have been developed, which achieve promising results [6–8]. Based on the results above, various machine learning methods have been used in tongue manifestation recognition or classification, such as support vector machine (SVM) [9–11], k Nearest Neighbor ( k -NN) [12, 13], Naive Bayes [11], Decision Tree [11], and Neural Network [9, 14].…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

Zhang

et al. 2017

BioMed Research International

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Objective. The purpose of this research is to develop a diagnostic method of diabetes based on standardized tongue image using support vector machine (SVM). Methods. Tongue images of 296 diabetic subjects and 531 nondiabetic subjects were collected by the TDA-1 digital tongue instrument. Tongue body and tongue coating were separated by the division-merging method and chrominance-threshold method. With extracted color and texture features of the tongue image as input variables, the diagnostic model of diabetes with SVM was trained. After optimizing the combination of SVM kernel parameters and input variables, the influences of the combinations on the model were analyzed. Results. After normalizing parameters of tongue images, the accuracy rate of diabetes predication was increased from 77.83% to 78.77%. The accuracy rate and area under curve (AUC) were not reduced after reducing the dimensions of tongue features with principal component analysis (PCA), while substantially saving the training time. During the training for selecting SVM parameters by genetic algorithm (GA), the accuracy rate of cross-validation was grown from 72% or so to 83.06%. Finally, we compare with several state-of-the-art algorithms, and experimental results show that our algorithm has the best predictive accuracy. Conclusions. The diagnostic method of diabetes on the basis of tongue images in Traditional Chinese Medicine (TCM) is of great value, indicating the feasibility of digitalized tongue diagnosis.

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Section: Introductionmentioning

confidence: 99%

Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

Zhang

et al. 2017

BioMed Research International

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“…With the development of computer and digital imaging techniques, image processing techniques have been widely studied in tongue diagnosis applications such as segmentation of the tongue image [2, 3], separation of tongue substance and tongue coating [4, 5], and analysis of tongue image [6].…”

Section: Introductionmentioning

confidence: 99%

“…In TCM, the tongue coating can indicate the location and nature of certain pathogens, furthermore, tongue colour can also reflect the condition of the stomach [11]. Thus, observation of the tongue coating is regarded as very important.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, observation of the tongue coating is regarded as very important. Kamarudin et al used the hue, saturation, and value colour threshold in tongue diagnosis to perform recognition analysis on the tongue's substance and coating [11]. Wang et al [12] introduced a method of evaluating the colour of the tongue surface that divided the tongue into different regions and excluded the tongue coating[13].…”

Section: Introductionmentioning

confidence: 99%

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Tongue colour and coating prediction in traditional Chinese medicine based on visible hyperspectral imaging

Zhang

Wang

et al. 2019

IET image process

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Tongue diagnosis is an important concept in Traditional Chinese Medicine (TCM). The tongue colour and coating can aid understanding of the body's physiological mechanisms, as well as the pathology of diseases. Existing research has focused on using digital images and tongue colour classification, without considering the other visible bands of information in the tongue. In this study, a visible hyperspectral image system, with an approximate spectral range of 400–1000 nm, was used to predict the tongue colour values and the coating position in TCM, and a stacked autoencoder (SAE) predict model based on spectral–spatial feature was performed to digital the tongue colour space and the coating. The experimental results show the effectiveness of the spectral–spatial feature with SAE model in predicting the CIELAB values of L, a, and coating position, thus the authors provide a new technique for the objective and digitising development of TCM.

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“…However, with the development of computer techniques, application of digital image processing techniques in tongue diagnosis has been widely studied, such as segmentation of the tongue image [1–3], separation of tongue substance and tongue coating [4, 5], and analysis of tongue image [6]. …”

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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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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Tongue's substance and coating recognition analysis using HSV color threshold in tongue diagnosis

Cited by 11 publications

References 0 publications

Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

Diagnostic Method of Diabetes Based on Support Vector Machine and Tongue Images

Tongue colour and coating prediction in traditional Chinese medicine based on visible hyperspectral imaging

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

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