• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.1
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• pp.97-103
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• 2009

The status and the property of a tongue are the important indicators to diagnose one's health like physiological and clinicopathological changes of inner organs. However, the tongue diagnosis is affected by examination circumstances like a light source, patient's posture, and doctor's condition. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, classifying tongue coating is inevitable but difficult since the features like color and texture of the tongue coatings and substance have little difference, especially in the neighborhood on the tongue surface. The proposed method has two procedures; the first is to acquire the color table to classify tongue coatings and substance by automatically separating coating regions marked by oriental medical doctors, decomposing the color components of the region into hue, saturation and brightness and obtaining the 2nd order discriminant with statistical data of hue and saturation corresponding to each kind of tongue coatings, and the other is to apply the tongue region in an input image to the color table, resulting in separating the regions of tongue coatings and classifying them automatically. As a result, kinds of tongue coatings and substance were segmented from a face image corresponding to regions marked by oriental medical doctors and the color table for classification took hue and saturation values as inputs and produced the classification of the values into white coating, yellow coating and substance in a digital tongue diagnosis system. The coating regions classified by the proposed method were almost the same to the marked regions. The exactness of classification was 83%, which is the degree of correspondence between what Oriental medical doctors diagnosed and what the proposed method classified. Since the classified regions provide effective information, the proposed method can be used to make an objective and standardized diagnosis and applied to an ubiquitous healthcare system. Therefore, the method will be able to be widely used in Oriental medicine.

• 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.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.13 no.2
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• pp.24-33
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• 2009

Objectives : This study was aimed to clarify the relationship between the tongue diagnosis of dampness-phlegm and hyperlipidemia in acute stroke patients. Methods : We analyzed the data of 1405 patients with acute stroke in 10 oriental medical hospitals from November 2006 to December 2008. We classified patients into two groups, dampness-phlegm and non dampness-phlegm by tongue diagnosis such as the white coating of the tongue, thick coating of the tongue, swollen tongue and teeth printed tongue which is oriental medical diagnosis. And We analyzed their characteristics with type of stroke and lipid blood level. Results : 1. There was no significant difference of stroke type between the dampness-phlegm tongue diagnosis group and the non dampness-phlegm tongue diagnosis group. 2. The ratio of SVO was higher in dampness-phlegm tongue diagnosis group (76.06%) than the non dampness-phlegm tongue diagnosis group (61.26%). 3. According to the blood test, the dampness-phlegm tongue diagnosis group showed higher in total cholesterol, HDL cholesterol, LDL cholesterol and triglyceride than the control group. 4. Total cholesterol and HDL cholesterol were significant higher in dampness-phlegm tongue diagnosis than the control group. Conclusion : According to the analysis, the relationship between the tongue diagnosis of dampness-phlegm and hyperlipidemia in acute stroke patients were more clarified. Based on these results, more prospective studies are to be done with more clinical data.

• Journal of Korean Traditional Oncology
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• v.27 no.1
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• pp.13-23
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• 2022

Objective: The purpose of this review is to analyze the clinical studies on tongue diagnosis in cancer patients. Methods: Domestic and foreign databases were used, such as Pubmed, google scholar, Wanfang med online, Scopus, and OASIS. Searching keywords were tongue diagnosis, tongue color, tongue fur, tongue inspection, cancer, tumor, neoplasm, carcinoma, etc. Studies on tongue diagnosis in cancer patients were included. The published year was limited from 2000 to June 2022. Results: Thirteen studies were enrolled. All selected studies were cross-sectional studies. Cancer patients tend to have a dark and blue-purple tongue, thick fur, yellow fur, fissure tongue, and red dots on the tongue compared with non-cancer patients. With the aggravation of cancer, the rate of patients having dark or blue, or purple tongues increased, and the patients' sublingual veins became wide and tortuous. Conclusion: This study suggests that cancer patients tend to have distinct features of tongue diagnosis. Further researches are warranted.

• Journal of Physiology & Pathology in Korean Medicine
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• v.19 no.5
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• pp.1437-1442
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• 2005

Tongue Diagnosis is the important traditional oriental medical diagnosis method that observes not only the general physiological state but also some kinds of disease. However, manual tongue diagnosis is much influenced by surrounding illumination. Therefore, Digital Tongue Inspection System(DigiTis) is needed for the quantification of objective tongue information, In this research, Tongue images of 98 malignant Neoplasm patients and 34 normal persons were collected by Digital Tongue Inspection System. Statistical analysis of tongue images and patient data indicates that cancer group has more blue-purple components in tongue body(舌質) and yellow components in tongue coating than normal group. Also, there are a lot of rose-pink components in the cancer group of second stage and blue-purple components in the cancer group of third or fourth stage. Our study shows that tongue image is a useful index for distinction between disease and health. Furthermore we need more extended research through the additional sampling and various disease.

• The Journal of Internal Korean Medicine
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• v.38 no.2
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• pp.152-163
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• 2017

Objectives: The aim of this review was to compare the findings of 14 studies of tongue diagnosis in Korean stroke patients. Methods: The purpose, subjects, observation methods, statistical methods, classification of tongue diagnosis, significance, and limitations were analyzed and compared. Results: In Korean stroke patients, tongue diagnosis is significant in pattern identifications, prognosis evaluation, degree of neurological deficit, past history, blood tests, and pulse diagnosis. Interobserver reliability of tongue diagnosis is relatively high.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.45 no.6
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• pp.123-131
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• 2008

In Oriental medicine, the status of a tongue is the important indicator to diagnose the condition of one's health like the physiological and the clinicopathological changes of internal organs in a body. A tongue diagnosis is not only convenient but also non-invasive, and therefore widely used in Oriental medicine. However, the tongue diagnosis is affected by examination circumstances like a light source, patient's posture, and doctor's condition a lot. To develop an automatic tongue diagnosis system for an objective and standardized diagnosis, segmenting a tongue region from a facial image captured and classifying tongue coating are inevitable but difficult since the colors of a tongue, lips, and skin in a mouth are similar. The proposed method includes preprocessing, over-segmenting, detecting the edge with a local minimum over a shading area from the structure of a tongue, correcting local minima or detecting the edge with the greatest color difference, selecting one edge to correspond to a tongue shape, and smoothing edges, where preprocessing consists of down-sampling to reduce computation time, histogram equalization, and edge enhancement, which produces the region of a segmented tongue. Finally, the systematic procedure separated only a tongue region from a face image with a tongue, which was obtained from a digital tongue diagnosis system. Oriental medical doctors' evaluation for the results illustrated that the segmented region excluding a non-tongue region provides important information for the accurate diagnosis. The proposed method can be used for an objective and standardized diagnosis and for an u-Healthcare system.

• Korean Journal of Oriental Medicine
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• v.14 no.3
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• pp.97-102
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• 2008

The tongue diagnosis is a diagnostic method in the oriental medicine that uses shape, substance, coating, and movement of the tongue to determine the condition of health and disease characteristics in human. Since this information, however, could be affected by subjective sense and visual information, it is difficult to obtain the objective and reproducible results. This research aims at building a reproducible tongue diagnosis system using color chart that is attached close to the face contact region. The picture of color chart is taken simultaneously with a tongue and applied to color revision. The system, in addition, is focused on providing a clear tongue image through securing a sufficient photographing distance with a surface coating mirror. The lightning part which can suppress the reflection by sputum in maximum is implemented for the objectification and quantification of the tongue diagnosis system. The face contact region is designed for consideration of a testee's convenience. To evaluate the reproducibility of the system, the CVs (coefficient of variance, %) of $L{\ast}$, $a{\ast}$ and $b{\ast}$ of red, green and blue regions in color chart are calculated, respectively. The results of all CVs shows that the tongue diagnosis system is re liable and those consequences contribute to the objectification and quantification of the tongue diagnosis system.

• Korean Journal of Oriental Medicine
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• v.14 no.3
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• pp.89-95
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• 2008

A tongue shows physiological and clinicopathological changes of inner organs. Visual inspection of a tongue is not only convenient but also non-invasive. To develop an automat ic tongue diagnosis system for an objective and standardized diagnosis, the separation of the tongue are a from a facial image and the detection of coatings, spots and cracks are inevitable but difficult since the colors of a tongue, lips, and skin in a mouth as well as those of tongue furs and body are similar. The propose d method includes preprocessing with down-sampling and edge enhancement, over-segmentation, detecting positions with a local minimum over shading from the structure of a tongue, and correcting local minima or detecting edge with color difference. The proposed method produces the region of a segmented tongue, and then decomposes the color components of the region into hue, saturation and brightness, resulting in classifying the regions of tongue furs(coatings) into kinds of coatings and substance and segmenting them. Spots are detected by using local maxima and the variation of saturation, and cracks are searched by using local minima and the directivity of dark areas in brightness. The results illustrate the segmented region with effective information, excluding a non-tongue region and also give us accurate discrimination of coatings and the precise detection of spots and cracks. It can be used to make an objective and standardized diagnosis for an u-Healthcare system as well as a home care system.

• Journal of dental hygiene science
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• v.20 no.4
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• pp.206-212
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• 2020

Background: A Teachable Machine is a kind of machine learning web-based tool for general persons. In this paper, the feasibility of Google's Teachable Machine (ver. 2.0) was studied in the diagnosis of the tooth-marked tongue. Methods: For machine learning of tooth-marked tongue diagnosis, a total of 1,250 tongue images were used on Kaggle's web site. Ninety percent of the images were used for the training data set, and the remaining 10% were used for the test data set. Using Google's Teachable Machine (ver. 2.0), machine learning was performed using separated images. To optimize the machine learning parameters, I measured the diagnosis accuracies according to the value of epoch, batch size, and learning rate. After hyper-parameter tuning, the ROC (receiver operating characteristic) analysis method determined the sensitivity (true positive rate, TPR) and specificity (false positive rate, FPR) of the machine learning model to diagnose the tooth-marked tongue. Results: To evaluate the usefulness of the Teachable Machine in clinical application, I used 634 tooth-marked tongue images and 491 no-marked tongue images for machine learning. When the epoch, batch size, and learning rate as hyper-parameters were 75, 0.0001, and 128, respectively, the accuracy of the tooth-marked tongue's diagnosis was best. The accuracies for the tooth-marked tongue and the no-marked tongue were 92.1% and 72.6%, respectively. And, the sensitivity (TPR) and specificity (FPR) were 0.92 and 0.28, respectively. Conclusion: These results are more accurate than Li's experimental results calculated with convolution neural network. Google's Teachable Machines show good performance by hyper-parameters tuning in the diagnosis of the tooth-marked tongue. We confirmed that the tool is useful for several clinical applications.