• 동의생리병리학회지
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• 제23권1호
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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.

• 전자공학회논문지
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• 제51권6호
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• pp.175-182
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• 2014

한방에서 설진은 초기 병증을 진단할 수 있는 방법으로 객관적인 진단 지표의 개발은 중요한 연구 분야 가운데 하나이다. 그러나 이전의 대부분의 설진 시스템은 한의사의 간섭을 배제한 자동 진단을 위한 전문가 시스템으로서 진료를 위한 객관화된 진단 지표의 생성에 있어서 어려움이 있었다. 따라서 본 논문에서는 한방 설진에 있어서 한의사의 진료 행위에 의해서 최적화된 설진을 위한 진단 지표를 계산하는 컴퓨터 지원 진단 소프트웨어를 제안한다. 개발한 소프트웨어는 반자동 방식으로 설질 추출을 위해서는 한의사의 매뉴얼 방식을 이용하고, 설태 영역을 자동 분할하여 비설태와 설태의 색상, WTCI 등에 대한 수치적 진단 지표를 한의사에게 실시간으로 제공한다. 또한, 설태 비율을 이용하여 소태, 박태, 후태의 분류 확률 추정 값과 진료 편의를 위한 전자의무기록 차트 기능도 제공한다. 구현된 컴퓨터 지원 진단 소프트웨어의 유효성 평가를 위해서는 60명의 피실험자를 대상으로 혀 영상을 구축하고, 설진 소프트웨어를 실험한 결과 피실험자의 95%가 소프트웨어 사용-유효성을 나타냈다.

• 대한한의진단학회지
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• 제13권2호
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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.

• 대한암한의학회지
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• 제27권1호
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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.

• 동의생리병리학회지
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• 제19권5호
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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.

• 대한한방내과학회지
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• 제38권2호
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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.

• 전자공학회논문지SC
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• 제45권6호
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• pp.123-131
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• 2008

한의학에서 혀의 상태는 인체 내부의 생리적 병리적 변화와 같은 건강 상태를 진단하는 중요한 지표로 활용된다. 혀의 상태를 진단하는 방법(설진)은 편리할 뿐 아니라 비침습적이므로, 한의학에서 널리 활용되고 있다. 하지만, 설진은 광원이나 환자의 자세, 의사의 건강 조건과 같은 검사 환경에 따라 많은 영향을 받는다. 객관적이고 표준화된 진단을 위한 자동 설진 시스템을 개발하기 위하여 촬영된 얼굴 영상으로부터 혀를 영역분할하고 설태를 분류하는 것은 필수적이지만 혀와 입술, 입 근처의 피부색이 서로 유사하므로 쉽지 않은 일이다. 제안된 방법은 전처리 과정과 영역분할, 혀의 구조로부터 발생하는 음영 영역의 지역 최소값 위치 검색, 지역 최소값의 교정, 컬러의 차이를 최대로 하는 위치를 찾는 컬러 경계면 탐색, 척의 기하적인 특성에 일치하는 경계면 선택, 경계면 평활화로 구성되어 있으며, 여기서 전처리 과정은 계산량의 감소를 위한 부 표본화, 히스토그램 평활화, 경계면 강화를 수행한다. 이러한 시스템적인 과정을 거치면, 영역분할된 혀를 획득할 수 있게 된다. 제안된 방법으로 분할된 영역은 초과적으로 혀가 아닌 영역을 제외해 낼 뿐 아니라 정확한 진단을 위해 중요한 정보를 제공함을 한의사의 진단 유효도 평가점수를 통해 확인할 수 있었다. 제안된 방법은 진단의 객관화와 표준화에 기여할 뿐만 아니라 u-Healthcare 시스템에도 활용 가능하다.

• 한국한의학연구원논문집
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• 제14권3호
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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.

• 한국한의학연구원논문집
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• 제14권3호
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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.

• 치위생과학회지
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• 제20권4호
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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.