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WTCI Tongue Coating Evaluation by analyzing a Ultraviolet Rays Tongue Image Channels (자외선 혀 영상 채널 분석에 의한 WTCI 설태 평가)

  • Lee, Woo-Beom
    • 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.

The Effects of Tongue Coating on Volatile Sulfur Compounds Production in the Oral Malodor Patients (구취 환자에서 설태가 휘발성 황화합물의 생성에 미치는 영향에 관한 연구)

  • Lee, Hun;Lee, Seung-Ryeul;Kim, Young-Ku
    • Journal of Oral Medicine and Pain
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    • v.26 no.3
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    • pp.243-252
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    • 2001
  • 본 연구에서는 구강 내 공기 중 설태 제거 전후의 휘발성 황화합물 농도를 gas chromatography를 이용하여 비교 분석하였다. 피검자로는 서울대학교 치과병원 구취클리닉에 내원한 환자 중에서 치주 건강 상태가 양호하며 구취를 호소하는 환자 18 명(평균연령 31.4세; 남자 8명, 여자 10명)을 대상으로 하였으며 구취를 측정하기 전에 모든 피검자들은 실험 전날 취침 전부터 실험 당일 실험시작 전까지 음식 섭취나 양치질 등의 모든 구강 활동을 금지하였다. 구취 시료는 채취 전에 피검자로 하여금 3분간 입을 다물게 한 후 입을 약 2cm정도 벌린 상태에서 시행하였으며 시료 채취 후 설태를 제거하였다. 설태 제거 후에 구강 내 공기를 다시 채취한 후 gas chromatography를 통하여 휘발성 황화합물의 각 성분별 농도를 분석하였다. 분석과정에서는 과거에 휘발성 황화합물의 검출 시 사용되어진 sampling loop와 isothermal run condition 대신 좀더 효율적인 직접표본주입방법과 oven temperature programmed analysis를 시행하였다. 1. 전체 휘발성 황화합물은 Hydrogen sulfide (59.96%), Methyl mercaptan (25.08%), Dimethyl sulfide (14.96%)로 구성되었다. 이 중 Hydrogen sulfide는 전체 휘발성 황화합물중 약 60%를 차지하여 치주상태가 양호한 구취환자에서의 주요한 구취 구 성 성분이었다. 2. 설태 제거 후 전체 휘발성 황화합물의 농도감소는 제거 전에 비하여 41.71%로 유의 하게 감소하였다(p<0.01). 3. 설태 제거 후에 Hydrogen sulfide의 농도감소는 43.62% (p<0.01), Methyl mercaptan 의 농도감소는 38.88% (p<0.05), 그리고 Dimethyl sulfide의 농도감소는 30.21% (p<0.01)로 각각 유의하게 감소하였다. 4. 전체 휘발성 황화합물의 구성비율은 설태 제거 전후에 유의한 차이가 없었다 (p>0.05).

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Classification of Tongue Coating for Tongue Diagnosis in Korean Medicine (한의학의 설진을 위한 설태 분류 방법)

  • Kim, Keun-Ho;Choi, Eun-Ji;Lee, Si-Woo;Kim, Jong-Yeol
    • Proceedings of the KIEE Conference
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    • pp.1985-1986
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    • 2008
  • 혀의 상태는 인체 내부의 생리적 병리적 특성의 변화를 나타내므로, 한의학에서 중요한 지수가 된다. 한의학에서 설진 방법은 환자의 설질과 설태의 변화를 관찰함으로써 질병을 진찰하는 방법이므로, 편리할 뿐만 아니라 비침습적이고, 널리 쓰이고 있다. 그러나 설진은 광원, 환자의 자세, 한의사의 상태와 같은 검사 환경에 의해 영향을 받는다. 표준화된 진단을 위한 자동 진단 시스템을 개발하기 위하여 질병의 예후를 판단할 수 있는 설태 분류 방법은 필수적이지만, 컬러의 경계가 모호하므로 설태와 설질을 구분하기는 매우 어렵다. 이 논문에서 분할된 설체 내에서 컬러를 계층적으로 분류하여 설태를 분류하는 방법을 제안한다. 또한 설태 영역을 정확하게 분할하도록 하였다. 제안된 방법은 표준화된 진단을 가능하도록 한다.

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Optimal Tongue Image Analysis for recognizing a Coated Tongue in the Tongue Diagnosis (설진에서 설태 인식을 위한 최적 혀 영상 분석)

  • Choi, chang-yur;Lee, woo-beom;Hong, you-sik;Lee, sang-suk;Nam, dong-hyun
    • Proceedings of the Korea Contents Association Conference
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    • pp.533-534
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    • 2011
  • 본 논문에서 적외선(IR; Infrared), 자외선(UV; Ultraviolet), 가시광선(VR; Visible ray)의 영역에서 촬영한 설진 영상으로부터 가장 효과적인 설태 인식을 위한 최적 혀 영상 분석 방법을 제안한다. 제안한 방법은 설진에서 혀 영상 촬영을 위한 최적 파장 범위와 해당 파장에서 설태 분석에 최적의 컬러 영상을 선정한다. 최적 영상 선정을 위해서는 각 파장별로 촬영한 혀 영상을 LAB, HSV, YcBcR, RGB 컬러모델로 변환하고, 변환된 영상들로부터 설태와 비설태 영역의 히스토그램(Histogram)을 분석에 의해서 영역-분별력을 측정한다. 실험 결과 설진에서 설태 인식을 위한 최적 혀 영상은 자외선 영역에서의 RGB 컬러모델로 나타났다.

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An Effectiveness Verification for Evaluating the Amount of WTCI Tongue Coating Using Deep Learning (딥러닝을 이용한 WTCI 설태량 평가를 위한 유효성 검증)

  • Lee, Woo-Beom
    • 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.

A Study on Halitosis by Oral Care Behavior and the Oral Environment (구강관리행동과 구강환경에 의한 구취에 관한 연구)

  • Jung, Su-Jin;Lee, Mi-Ra
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.17 no.1
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    • pp.629-637
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    • 2016
  • This study examined the tongue coating index and halitosis to determine the association with the oral care behavior and variation in halitosis according to the tongue coating index, dental calculus, and the oral environment before and after scaling among 130 participants in scaling practices of the Department of Dental Hygiene at K University in Daejeon. The subjects were asked to participate in a survey, in an oral examination for the tongue coating index, dental plaque, and dental calculus status, and in halitosis measurement. The tongue coating most significantly affected halitosis and the tongue coating index was strongly correlated with smoking and tongue washing. More frequent toothbrushing, a lower level of halitosis; and nonsurgical treatment of scaling were effective in reducing halitosis; age was associated with the tongue coating index. Therefore, it will be necessary to perform good oral care and reduce the amount of dental plaque and tongue coating and undergo regular scaling with the objective of improving oral health and reducing halitosis.

Coated Tongue Region Extraction using the Fluorescence Response of the Tongue Coating by Ultraviolet Light Source (설태의 자외선 형광 반응을 이용한 설태 영역 추출)

  • Choi, Chang-Yur;Lee, Woo-Beom;Hong, You-Sik;Nam, Dong-Hyun;Lee, Sang-Suk
    • The Journal of the Institute of Internet, Broadcasting and Communication
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    • v.12 no.4
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    • pp.181-188
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    • 2012
  • An effective extraction method for extracting a coated tongue is proposed in this paper, which is used as the diagnostic criteria in the tongue diagnosis. Proposed method uses the fluorescence response characteristics of the coated tongue that is occurred by using the ultraviolet light. Specially, this method can solved the previous problems including the issue in the limits of the diagnosis environment and in the objectivity of the diagnosis results. In our method, original tongue image is acquired by using the ultraviolet light, and binarization is performed by thresholding a valley-points in the histogram that corresponds to the color difference of tongue body and tongue coating. Final view image is presented to the oriental doctor, after applying the canny-edge algorithm to the binary image, and edge image is added to the original image. In order to evaluate the performance of the our proposed method, after building a various tongue image, we compared the true region of coated tongue by the oriental doctor's hand with the extracted region by the our method. As a result, the proposed method showed the average 87.87% extraction ratio. The shape of the extracted coated tongue region showed also significantly higher similarity.

Development of Tongue Diagnosis System Using ASM and SVM (ASM과 SVM을 이용한 설진 시스템 개발)

  • Park, Jin-Woong;Kang, Sun-Kyung;Kim, Young-Un;Jung, Sung-Tae
    • Journal of the Korea Society of Computer and Information
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    • v.18 no.4
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    • pp.45-55
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    • 2013
  • In this study, we propose a tongue diagnosis system which detects the tongue from face image and divides the tongue area into six areas, and finally generates tongue fur ratio of each area. To detect the tongue area from face image, we use ASM as one of the active shape models. Detected tongue area is divided into six areas and the distribution of tongue coating of six areas is examined by SVM. For SVM, we use a 3-dimensional vector calculated by PCA from a 12-dimensional vector consisting of RGB, HSV, Lab, and Luv. As a result, we stably detected the tongue area using ASM. Furthermore, we recognized that PCA and SVM helped to raise the ratio of tongue coating detection.