Study of Factors Involved in Tongue Color Diagnosis by Kampo Medical Practitioners Using the Farnsworth‐Munsell 100 Hue Test and Tongue Color Images

Oji, Takeshi; Namiki, Takao; Nakaguchi, Toshiya; Ueda, Keigo; Takeda, Kanako; Nakamura, Michimi; Okamoto, Hideki; Hirasaki, Yoshiro

doi:10.1155/2014/783102

Cited by 20 publications

(17 citation statements)

References 27 publications

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“…Those that received the agreement from all three professionals would be included in this study. According to the literature's reporting, tongue color would be classified into five groups, namely, pale tongue, light red tongue, red tongue, crimson tongue, and purplish tongue (including light purple tongue and blue and purple tongue) [15, 19]. …”

Section: Methodsmentioning

confidence: 99%

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The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

et al. 2016

BioMed Research International

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Background and Goal. The application of digital image processing techniques and machine learning methods in tongue image classification in Traditional Chinese Medicine (TCM) has been widely studied nowadays. However, it is difficult for the outcomes to generalize because of lack of color reproducibility and image standardization. Our study aims at the exploration of tongue colors classification with a standardized tongue image acquisition process and color correction. Methods. Three traditional Chinese medical experts are chosen to identify the selected tongue pictures taken by the TDA-1 tongue imaging device in TIFF format through ICC profile correction. Then we compare the mean value of L * a * b * of different tongue colors and evaluate the effect of the tongue color classification by machine learning methods. Results. The L * a * b * values of the five tongue colors are statistically different. Random forest method has a better performance than SVM in classification. SMOTE algorithm can increase classification accuracy by solving the imbalance of the varied color samples. Conclusions. At the premise of standardized tongue acquisition and color reproduction, preliminary objectification of tongue color classification in Traditional Chinese Medicine (TCM) is feasible.

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Section: Methodsmentioning

confidence: 99%

“…Oji et al once studied the color discrimination and tongue color diagnosis in 68 traditional Japanese medical practitioners by the Farnsworth-Munsell 100 Hue test and 84 tongue images, respectively, and they found out that overall color discrimination became worse with aging [19]. …”

Section: Introductionmentioning

confidence: 99%

The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

et al. 2016

BioMed Research International

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“…So, it is difficult for tongue diagnosis to obtain objective and standardized results. In the past decades, researchers have developed various types of computer-aided tongue diagnosis systems (CATDS) based on computer vision and machine learning to address these problems in many countries [3][4][5][6]. The schematic diagram of a typical CATDS is shown in Figure 1 [7].…”

Section: Introductionmentioning

confidence: 99%

TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine

Huang

Zhang

et al. 2020

Computational and Mathematical Methods in Medicine

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Extracting the tongue body accurately from a digital tongue image is a challenge for automated tongue diagnoses, as the blurred edge of the tongue body, interference of pathological details, and the huge difference in the size and shape of the tongue. In this study, an automated tongue image segmentation method using enhanced fully convolutional network with encoder-decoder structure was presented. In the frame of the proposed network, the deep residual network was adopted as an encoder to obtain dense feature maps, and a Receptive Field Block was assembled behind the encoder. Receptive Field Block can capture adequate global contextual prior because of its structure of the multibranch convolution layers with varying kernels. Moreover, the Feature Pyramid Network was used as a decoder to fuse multiscale feature maps for gathering sufficient positional information to recover the clear contour of the tongue body. The quantitative evaluation of the segmentation results of 300 tongue images from the SIPL-tongue dataset showed that the average Hausdorff Distance, average Symmetric Mean Absolute Surface Distance, average Dice Similarity Coefficient, average precision, average sensitivity, and average specificity were 11.2963, 3.4737, 97.26%, 95.66%, 98.97%, and 98.68%, respectively. The proposed method achieved the best performance compared with the other four deep-learning-based segmentation methods (including SegNet, FCN, PSPNet, and DeepLab v3+). There were also similar results on the HIT-tongue dataset. The experimental results demonstrated that the proposed method can achieve accurate tongue image segmentation and meet the practical requirements of automated tongue diagnoses.

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“…In addition, because the description of tongue findings in various books describing tongue diagnosis are diverse and inconsistent, studies about unifying the diagnosis description have been conducted [14], and attempts to standardize tongue diagnosis are being made. It has been reported that it takes more than 10 years of Kampo diagnosis practice to be able to make an accurate diagnosis without being affected by subjective factors such as age and color discrimination ability [15], and it is not easy for doctors with little experience to judge the findings. In order to obtain accurate SB findings on the tongue, quantitative indicators that support diagnosis and systems that can be used in education are necessary.…”

Section: Introductionmentioning

confidence: 99%

Assessing the efficacy of a tongue image analyzing system (TIAS) for the objective diagnosis of static blood: An observational, retrospective, single‐center study of Japanese Kampo medicine

Kainuma

Matsumura

Kawashiri

et al. 2020

Traditional & Kampo Medicine

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Aim: Static blood (SB) is an important pathological concept of Kampo medicine. Although tongue findings are very useful in the diagnosis of SB, the relation between the severity of SB and tongue color (TC) is unclear. The purpose of this study was to evaluate the relation between the severity of SB and TC obtained by the use of a Tongue image analyzing system (TIAS). Methods: We analyzed data from 227 new patients (age 20-85) who visited the Kyushu University Hospital Kampo Clinic between 2013 and 2018. The SB score was calculated based on the diagnostic criteria by Terasawa et al. and classified into three groups: non-static blood (NSB) ≤20 points, SB 20.5 to 39.5 points, severe static blood (SSB) ≥40 points. TC was measured by the device-independent international commission on Illumination 1976L*a*b* color space standards at four points: (i) edge; (ii) posterior; (iii) middle; and (iv) apex. Results: SB and SSB occurred significantly more frequently in women and patients were of younger age than in the NSB group (P < 0.05). Analysis of TC found 1L* and 4L* to be significantly lower (P < 0.05), and 1a*, 2a*, 3a*, and 4a* significantly higher in SSB than in NSB (P < 0.05). The cutoff value of 2a* and the combination of 1L* + 1a* for SSB prediction obtained from the ROC curve were 2a*

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Study of Factors Involved in Tongue Color Diagnosis by Kampo Medical Practitioners Using the Farnsworth‐Munsell 100 Hue Test and Tongue Color Images

Cited by 20 publications

References 27 publications

The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

The Classification of Tongue Colors with Standardized Acquisition and ICC Profile Correction in Traditional Chinese Medicine

TISNet-Enhanced Fully Convolutional Network with Encoder-Decoder Structure for Tongue Image Segmentation in Traditional Chinese Medicine

Assessing the efficacy of a tongue image analyzing system (TIAS) for the objective diagnosis of static blood: An observational, retrospective, single‐center study of Japanese Kampo medicine

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