Coated Tongue Region Extraction using the Fluorescence Response of the Tongue Coating by Ultraviolet Light Source

Choi, Chang-Yur; Lee, Woo-Beom; Hong, Yousik; Nam, Dong Hyun; Lee, Sang-Suk

doi:10.7236/jiwit.2012.12.4.181

The Journal of the Institute of Webcasting, Internet and Teleco

2012

DOI: 10.7236/jiwit.2012.12.4.181

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Coated Tongue Region Extraction using the Fluorescence Response of the Tongue Coating by Ultraviolet Light Source

Chang-Yur Choi¹,

Woo-Beom Lee²,

Yousik Hong³

et al.

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Cited by 3 publications

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Analysis of Quantitative Light-Induced Fluorescence Images for the Assessment of Bacterial Activity and Distribution of Tongue Coating

Kim

Kang

2023

Healthcare

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This study aimed to verify and to validate the correlation between, and validate the assessment of, bacterial activity and the distribution of tongue coating, by analyzing quantitative light-induced fluorescence (QLF) images for the diagnosis of oral malodor. Oral malodor was measured using the Twin Breasor II, and bacterial activity and the distribution of tongue coating were investigated using QLF images. Bacterial activity and the distribution of tongue coating were quantitatively analyzed by dividing the tongue into six areas using the TB01 1.05 software. ROC curves were generated using MedCalc® software to validate the diagnosis of oral malodor, the testing of bacterial activity, and the distribution of tongue coating. Bacterial activity and the distribution of tongue coating showed a very strong association with each other (r = 0.937, p < 0.01), and were significantly higher in the oral malodor group (p < 0.05). The distribution of tongue coating was highly correlated with H2S (r = 0.223, p < 0.01) but not associated with CH3SH. Sensitivity, specificity, and the area under the curve confirmed the high accuracy of this method in assessing bacterial activity and the distribution of tongue coating in the diagnosis of oral malodor. Since QLF images provide significant accuracy during quantitative analysis in the identification of bacterial activity and the distribution of tongue coating, systematic management of tongue coating and reduced oral malodor can be achieved by actively using QLF images and oral malodor measurement.

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Analysis of Quantitative Light-Induced Fluorescence Images for the Assessment of Bacterial Activity and Distribution of Tongue Coating

Kim

Kang

2023

Healthcare

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Implementation of Computerized Assistant Diagnosis Software for Tongue Diagnosis in the Oriental Medicine

이우범¹

2014

Journal of the Institute of Electronics and Information Enginee

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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.

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Development of Tongue Diagnosis System Using ASM and SVM

Park¹,

Kang²,

Kim³

et al. 2013

Journal of the Korea Society of Computer and Information

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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.

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Coated Tongue Region Extraction using the Fluorescence Response of the Tongue Coating by Ultraviolet Light Source

Cited by 3 publications

References 4 publications

Analysis of Quantitative Light-Induced Fluorescence Images for the Assessment of Bacterial Activity and Distribution of Tongue Coating

Analysis of Quantitative Light-Induced Fluorescence Images for the Assessment of Bacterial Activity and Distribution of Tongue Coating

Implementation of Computerized Assistant Diagnosis Software for Tongue Diagnosis in the Oriental Medicine

Development of Tongue Diagnosis System Using ASM and SVM

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