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Tongue color clustering and visual application based on 2D information

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Abstract

Purpose

Studies have shown the association between tongue color and diseases. To help clinicians make more objective and accurate decisions quickly, we take unsupervised learning to deal with the basic clustering of tongue color in a 2D way.

Methods

A total of 595 typical tongue images were analyzed. The 3D information extracted from the image was transformed into 2D information by principal component analysis (PCA). K-Means was applied for clustering into four diagnostic groups. The results were evaluated by clustering accuracy (CA), Jaccard similarity coefficient (JSC), and adjusted rand index (ARI).

Results

The new 2D information totally retained 89.63% original information in the L*a*b* color space. And our methods successfully classified tongue images into four clusters and the CA, ARI, and JSC were 89.04%, 0.721, and 0.890, respectively.

Conclusions

The 2D information of tongue color can be used for clustering and to improve the visualization. K-Means combined with PCA could be used for tongue color classification and diagnosis. Methods in the paper might provide reference for the other research based on image diagnosis technology.

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References

  1. Zhang B, Wang X-z, You J, Zhang D (2013) Tongue color analysis for medical application. Evidence-based complementary and alternative medicine. Evid Based Complement Altern Med 2013:1–11

    Google Scholar 

  2. Li B, Huang Q, Lu Y, Chen S, Liang R, Wang Z (2008) A method of classifying tongue colors for traditional Chinese medicine diagnosis based on the CIELAB color space. In: International conference on medical biometrics. Springer

  3. ISO 20498-5, Traditional Chinese medicine—computerized tongue image analysis system—part 5: method of acquisition and expression of tongue colour and tongue coating colour

  4. Hao J, Zhu C-h, Cao R, Yang X, Ding X-l, Man Y, Xiong-zhi W (2016) Purple-bluish tongue is associated with platelet counts, and the recurrence of epithelial ovarian cancer. J Tradit Chin Med 36(3):321–325

    Article  Google Scholar 

  5. Jung C, Kim K, Boncho K, Kim J (2015) Trends in tongue color of subtype patterns on deficiency syndrome. Integr Med Res 4(1):29

    Article  Google Scholar 

  6. Kainuma M, Furusyo N, Urita Y, Nagata M, Ihara T, Oji T, Nakaguchi T, Namiki T, Hayashi J (2015) The association between objective tongue color and endoscopic findings: results from the Kyushu and Okinawa population study (KOPS). BMC Complement Altern Med 15(1):372

    Article  Google Scholar 

  7. Hsieh SF, Shen LL, Su SY (2016) Tongue color changes within a menstrual cycle in eumenorrheic women. J Tradit Complement Med 6(3):269–274

    Article  Google Scholar 

  8. Liu W-l, Zhang H-y, Che N-c, Tang Z-q, Gao L-y (2010) Changes of color and blood flow of the tongue in the mini-swine of immune hepatic injury. J Tradit Chin Med 30(4):265–271

    Article  Google Scholar 

  9. Qi Z, Li-ping T, Chen J-b, Xiao-juan H, Jia-tuo X, Zhang Z-f (2016) The classification of tongue colors with standardized acquisition and ICC profile correction in traditional Chinese medicine. Biomed Res Int 2016(1):1–9

    Google Scholar 

  10. Kawanabe T, Kamarudin ND, Ooi CY, Kobayashi F, Mi X, Sekine M, Wakasugi A, Odaguchi H, Hanawa T (2016) Quantification of tongue colour using machine learning in Kampo medicine. Eur J Integr Med 8(6):932–941

    Article  Google Scholar 

  11. Hou J, Su H-Y, Yan B, Zheng H, Sun Z-L, Cai X-C (2017) Classification of tongue color based on CNN. In: IEEE international conference on big data analysis. IEEE

  12. Wan S, Zhen-ye X, Wang Z-q, Jia-tuo X (2011) Objectified study on tongue images of patients with lung cancer of different syndromes. Chin J Integr Med 18(4):272–276

    Google Scholar 

  13. Lee JA, Ko MM, Kang B-K, Alraek T, Birch S, Lee MS (2012) Interobserver reliability of four diagnostic methods using traditional korean medicine for stroke patients. J Altern Complem Med 18(1):29–34

    Article  Google Scholar 

  14. Li Y, Linde K, Dai J, Zhang J, Hager SM, Saller R, Melchart D (2009) On the reliability of the description of morphological characteristics in traditional Chinese tongue diagnostics. Forschende Komplementärmedizin/Res Complement Med 16(2):98–104

    Google Scholar 

  15. Lo L-c, Chen Y-f, Chen W-j, Cheng T-l, Chiang JY (2012) The study on the agreement between automatic tongue diagnosis system and traditional Chinese medicine practitioners. Evid Based Complem Altern Med 2012:505063

    Google Scholar 

  16. Kim M, Cobbin D, Zaslawski C (2008) Traditional Chinese medicine tongue inspection: an examination of the inter- and intrapractitioner reliability for specific tongue characteristics. J Altern Complement Med 14(5):527–536

    Article  Google Scholar 

  17. Xu J, Tu L, Zhang Z, Zhang L, Zhou C (2008) The region partition of quality and coating for tongue image based on color image segmentation method. In: IEEE international symposium on IT in Medicine and Education, pp 817–821

  18. Xu J, Zhou C, Zhang Z, Fang Z, Fei Z, Bao Y, Li W, Sun Y (2004) Computerized analysis and recognition of tongue and its coating color in tongue diagnosis. J Shanghai Univ Tradit Chin Med 18(3):43–47

    Google Scholar 

  19. Zhou Z-H (2016) Machine learning. Tsinghua University Press, Beijing

    Google Scholar 

  20. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: machine learning in Python. J Mach Learn Res 12:2825–2830

    Google Scholar 

  21. Qi Z, Tu L, Luo Z, Hu X, Zeng L, Jiao W, Ma X, Jing C, Wang W, Zhang Z, Xu J (2018) Tongue image database construction based on the expert opinions: assessment for individual agreement and methods for expert selection. Evid Based Complement Altern Med. https://doi.org/10.1155/2018/8491057

    Article  Google Scholar 

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Funding

This study was funded by the National Natural Science Foundation of China (Grant Numbers: 81373556, 81102558, and 81873235) and National Key Research and Development Program of China (Grant Number: 2017YFC1703300 and 2017YFC1703301).

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Authors and Affiliations

  1. School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China

    Wen Jiao, Li-ping Tu, Zhen Qi, Zhi-yu Luo, Ling-zhi Zeng, Xu-xiang Ma, Ching-Hsuan Pai, Hong-yuan Fu, Yu Wang, Jue Wang & Jia-tuo Xu

  2. Shanghai Collaborative Innovation Center of Health Service in Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China

    Xiao-juan Hu

  3. Department of Intelligent Science and Technology, Xiamen University, 422 South Siming Road, Xiamen, 361005, Fujian, China

    Chang-le Zhou

Authors
  1. Wen Jiao
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  2. Xiao-juan Hu
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  3. Li-ping Tu
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  4. Chang-le Zhou
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  5. Zhen Qi
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  7. Ling-zhi Zeng
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  8. Xu-xiang Ma
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  9. Ching-Hsuan Pai
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  10. Hong-yuan Fu
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  11. Yu Wang
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  12. Jue Wang
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  13. Jia-tuo Xu
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Contributions

WJ, XH, and LT contributed equally to this work. WJ wrote the manuscript, XH helped with machine learning, and LT assisted in data analysis. CZ, ZQ, ZL, LZ, XM, C-HP, HF, YW, and JW enrolled study subjects, collected data, and other work. J-TX designed the study and approved the manuscript before submission. All authors had read the final version of this manuscript and approved the manuscript before submission.

Corresponding author

Correspondence to Jia-tuo Xu.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

IRB of Shuguang Hospital affiliated with Shanghai University of TCM approved the study (No. 2015-388-16-01). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Jiao, W., Hu, Xj., Tu, Lp. et al. Tongue color clustering and visual application based on 2D information. Int J CARS 15, 203–212 (2020). https://doi.org/10.1007/s11548-019-02076-z

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  • DOI: https://doi.org/10.1007/s11548-019-02076-z

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