• Journal of the Institute of Convergence Signal Processing
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• v.16 no.3
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• pp.96-101
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• 2015

A tongue coating evaluation method for WTCI(Winkel Tongue Coating Index) is proposed in this paper, which is used as the diagnostic criteria in the tongue diagnosis. This method uses the color channel analysis and tongue coating extraction from the ultraviolet tongue image. Proposed method analyzes the histogram distribution of the respective color channel for extracting a tongue coating, and performs the verification test from the selected color channel in the tongue coating extraction. Also, Objectivity of the tongue diagnostic criteria is verified by the artificial sample and real-tongue image experiments. In order to evaluate the performance of the proposed Computerized Assistant WTCI Evaluation method, after verifying a measurement accuracy by using the artificial sample images, and applying to the various real-tongue image of subjects. As a result, the proposed WTCI method is very successful.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.4
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• pp.226-231
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• 2019

A WTCI is an important criteria for evaluating an mount of patient's tongue coating in tongue diagnosis. However, Previous WTCI tongue coating evaluation methods is a most of quantitatively measuring ration of the extracted tongue coating region and tongue body region, which has a non-objective measurement problem occurring by exposure conditions of tongue image or the recognition performance of tongue coating. Therefore, a WTCI based on deep learning is proposed for classifying an amount of tonger coating in this paper. This is applying the AI deep learning method using big data. to WTCI for evaluating an amount of tonger coating. In order to verify the effectiveness performance of the deep learning in tongue coating evaluating method, we classify the 3 types class(no coating, some coating, intense coating) of an amount of tongue coating by using CNN model. As a results by testing a building the tongue coating sample images for learning and verification of CNN model, proposed method is showed 96.7% with respect to the accuracy of classifying an amount of tongue coating.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.6
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• pp.175-182
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• 2014

Development of an objective diagnosis index for diagnosing a the beginning nature of a disease is the most one of tongue diagnosis in the oriental medicine. However, previous systems have a difficult problem in the creation of objective diagnosis index, and focused on the expert system that can diagnose automatically without an oriental doctor behavior. Therefore, computerized assistant diagnosis software for calculating an optimized diagnosis index is proposed in this paper. This software is operated by the diagnosing behavior of oriental doctor. As developed software is a semi-automatic system, manual method is used to segment a tongue body. Futhermore, numerical diagnosis indices including the color information of non-tongue coating and tongue coating, WTCI are provided to oriental doctor automatically and real-timely. Also, probability estimation value for classifying no coating, thin coating, and thick coating is presented by using the tongue coating area ratio, and EMR chart can use for convenience of diagnosis. In order to evaluate the effectiveness of the our developed software, after building a various tongue image from 60 subjects, we experimented on diagnosis image with our software. As a result, the developed software showed the 95% use-effectiveness of subjects.

• Journal of Korean Medicine for Obesity Research
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• v.20 no.1
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• pp.40-51
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• 2020

Objectives: We aimed to compare the quality of sleep, tongue diagnosis, oral microbiology differences in insomnia of Liver qi stagnation (LQS) and Non-Liver qi stagnation (NLQS). Methods: 56 patients were classified as LQS or NLSQ type insomnia through the insomnia differentiation questionnaire. The depression scores between the groups were compared through beck depression inventory (BDI), and the sleep quality was compared through Pittsburgh sleep quality index (PSQI) and Insomnia Severity Index (ISI). We analyzed the sleep efficiency, total sleep time, total awake frequency, total and average awake time through actigraph. For the tongue diagnosis, the distribution of tongue coating in six areas were measured through Winkel tongue coating index (WTCI). Linear discriminant analysis was performed to observe the differences in composition of microbial strains between the groups. Results: The scores of BDI, ISI and PSQI were significantly higher in LQS group. The total sleep time in LQS group was significantly less than that of NLQS group. Among the areas of tongue, according to the WTCI, the amount of tongue coating in zones A and C was significantly small. In oral microbial analysis, there was no significant difference between the groups at the phylum level. At the genus level, Prevotella, Veillonella, and Streptococcus were predominant in LQS group, whereas Prevotella, Neisseria, and Streptococcus in NLQS group. Conclusions: It was meaningful that insomnia was more likely in LQS group than in NLQS group, and the composition of oral microorganisms was significantly different, which could lead to the diseases caused by stress.

• The Journal of Internal Korean Medicine
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• v.35 no.4
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• pp.483-497
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• 2014

Objectives: The aims of study were developing cut-off value of Yin-deficiency questionnaire (YDQ) for diagnosis of Yin-deficiency (YD) and compare diagnostic ability between YDQ and Yin-deficiency scale score (YDS) in xerostomia patients. Methods: We recruited 58 xerostomia patients. They were diagnosed YD or non-YD by 3 Korean medicine doctors (KMD). We assessed YD using YDQ and YDS. We evaluated xerostomia using VAS, Dry Mouth Symptom Questionnaire (DMSQ), Salivary Flow Rate (SFR), oral moisture on buccal mucosa and tongue surface (OMB and OMT). We surveyed tongue coatings using Winkel Tongue Coating Index (WTCI). Results: We diagnosed 23 patients YD and 35 patients non-YD. There were no significant differences of age, sex and body mass index between the YD and non-YD groups. Using receiver operating characteristic curve analysis, the optimal cut-off value of YDQ was defined as 304. Sensitivity, specificity and Youden index of YDQ were 86.96%, 71.43% and 1.5839 respectively. Using Cohen's coefficient of agreement, we found that degree of agreement between KMD and YDQ diagnosis was moderate (${\kappa}$=0.524, p<0.001). Using Pearson's correlation analysis, we found concurrent validity of YDQ and YDS were significant correlated. Using area under curve value, we found diagnostic ability between YDQ and YDS were not significantly different (p=0.505), but there were more strong correlations between DMSQ-symptoms and YDQ (r=0.731, p<0.001) than correlations between DMSQ-symptoms and YDS (r=0.418, p<0.01). Conclusions: The cut-off value of YDQ can diagnose YD in xerostomia and diagnostic ability of YDQ in xerostomia is better than YDS.