Quantification of tongue colour using machine learning in Kampo medicine

Kawanabe, Tadaaki; Kamarudin, Nur Diyana; Ooi, Chia Yee; Kobayashi, Fuminori; Mi, Xiaoyu; Sekine, Mariko; Wakasugi, Akino; Odaguchi, Hiroshi; Hanawa, Toshihiko

doi:10.1016/j.eujim.2016.04.002

Cited by 39 publications

(33 citation statements)

References 26 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%

“…Their research can be grouped into four categories, shape [7–9], texture [10], color [11–15], and comprehensive feature analysis of tongue images [16, 17]. …”

Section: Introductionmentioning

confidence: 99%

“…According to two different color measurement systems, studies on the objective classification of tongue color are mainly implemented through the visible reflection spectrum [12, 13] and the commonly used pixelwise or color space, respectively [14, 15]. …”

Section: Introductionmentioning

confidence: 99%

“…They used K -means clustering algorithm to quantify tongue body and color coating information and concluded that clinically important tongue color differences in Kampo medicine can be visualized by applying machine learning to tongue images that are taken under stable conditions [15]. …”

Section: Introductionmentioning

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%

“…Their research can be grouped into four categories, shape [7–9], texture [10], color [11–15], and comprehensive feature analysis of tongue images [16, 17]. …”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…However, the use of only one sample makes the system incompetent. The Sobel is another popular filter to be found in the literature 56, 59. Few works have been done using mathematical morphology.…”

Section: Image Segmentation and Feature Extractionmentioning

confidence: 99%

Advances in automated tongue diagnosis techniques

Tania

Lwin

2019

Integrative Medicine Research

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Tongue diagnosis can be an effective, noninvasive method to perform an auxiliary diagnosis any time anywhere, which can support the global need in the primary healthcare system. This work reviews the recent advances in tongue diagnosis, which is a significant constituent of traditional oriental medicinal technology, and explores the literature to evaluate the works done on the various aspects of computerized tongue diagnosis, namely preprocessing, tongue detection, segmentation, feature extraction, tongue analysis, especially in traditional Chinese medicine (TCM). In spite of huge volume of work done on automatic tongue diagnosis (ATD), there is a lack of adequate survey, especially to combine it with the current diagnosis trends. This paper studies the merits, capabilities, and associated research gaps in current works on ATD systems. After exploring the algorithms used in tongue diagnosis, the current trend and global requirements in health domain motivates us to propose a conceptual framework for the automated tongue diagnostic system on mobile enabled platform. This framework will be able to connect tongue diagnosis with the future point-of-care health system.

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Computational tongue color simulation in tongue diagnosis

Chen

Jiang

et al. 2021

Color Research & Application

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Tongue color is one of the important indices for tongue diagnosis in traditional Chinese medicine. This study aims to analyze tongue colors of computational tongue diagnosis in traditional Chinese medicine using scientific quantification and computational simulation. The tongue color data are established according to the experiment in which the doctors who use Chinese traditional medicine assessed standard Munsell color charts under the standard lighting environment. Tongue color is classified into six color names, which are pale red, light red, red, crimson, dark red, and purple. The doctors assessed Munsell color charts to find the corresponding color charts' distributions of each color name in tongue color. The hue‐lightness‐chroma data of the chosen Munsell color charts were transformed to CIE xyY using the look‐up table computation, then further were converted to CIELAB values and sRGB data. Based on the 95% confidence ellipses formed on CIELAB (a*, b*) plane and CIELAB (C*, L*) plane, the comparisons between tongue colors and general colors were analyzed. The computational tongue image simulation combining the elements of color, texture, and moisture was successfully established. This computational simulation method could potentially become a useful tool for teaching and learning diagnoses in the education of Chinese medicine.

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Quantification of tongue colour using machine learning in Kampo medicine

Cited by 39 publications

References 26 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

Advances in automated tongue diagnosis techniques

Computational tongue color simulation in tongue diagnosis

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