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Principal component vector rotation of the tongue color spectrum to predict “Mibyou” (disease-oriented state)

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Kampo medicine (Japanese traditional herbal medicine) contains concepts useful for preventive medicine. For example, “Mibyou” (disease-oriented state) aims to prevent illness by early recognition. Kampo diagnosis is based on subjective examinations, such as tongue inspection, by trained specialist physicians. An objective metric of the tongue color spectrum was developed as a surrogate for subjective visual inspection.

Methods

Tongue images were acquired with a hyperspectral imaging system, and the uncoated tongue region was segmented automatically. The spectral information of the uncoated tongue area was analyzed by principal component analysis (PCA). The component vector most representative of each clinical symptom was found by rotating the vector on a plane spanned by two arbitrary principal component vectors.

Results

The system was tested in human volunteers. Forty-four hyperspectral images were acquired from 30 healthy male subjects for initial testing. The Oketsu (blood stagnation) score was determined by an experienced clinician in Kampo medicine from 27 of 30 subjects. The correlation between respective principal components and Oketsu score was 0.67 at maximum, and increased to 0.73 by linear combination, while it was −0.75 by vector rotation. Significant correlations for many disorders were demonstrated, and vector rotation showed better correlation than linear combination.

Conclusions

A PCA-based algorithm was developed to objectively evaluate patients using color images of the tongue surface. Testing showed that this method was a feasible surrogate for expert visual tongue analysis. This tool should help non-trained people identify “Mibyou” health status for individuals. The algorithm is free of empirical criteria, and it may be it applicable to many hyperspectral image types.

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References

  1. Terasawa K (1993) KAMPO: Japanese–oriental medicine—insights from clinical cases. KK Standard Mclntyre

  2. Sato Y, Hanawa T, Arai M, Cyong J, Fukuzawa M, Mitani K, Ogihara Y, Sakiyama T, Shimada Y, Toriizuka K et al (2005) Introduction to Kampo—Japanese traditional medicine. The Japan society for oriental medicine. Elsevier, Tokyo

    Google Scholar 

  3. Yamamoto S, Tsumura N, Nakaguchi T, Namiki T, Kasahara Y, Terasawa K, Miyake Y (2010) Regional image analysis of the tongue color spectrum. Int J Comput Assist Radiol Surg. doi:10.1007/s11548-010-0492-x

  4. Liu Z, Yan J, Zhang D, Li Q (2007) Automated tongue segmentation in hyperspectral images for medicine. Appl Opt 46(34): 8328–8334

    Article  PubMed  Google Scholar 

  5. Liu Z, Li Q, Yan J, Tang Q (2007) A novel hyperspectral medical sensor for tongue diagnosis. Sensor Rev 27(1): 57–60

    Article  Google Scholar 

  6. Li Q, Xue Y, Wang J, Yue X (2007) Automated tongue segmentation algorithm based on hyperspectral image. J Infrared Millim Waves (Hongwai yu Haomibo Xuebao) 26:77–80, (in Chinese)

    Google Scholar 

  7. Liu Z, Zhang D, Yan J, Li Q, Tang Q (2007) Classification of hyperspectral medical tongue images for tongue diagnosis. Comput Med Imaging Graph 31(8): 672–678

    Article  Google Scholar 

  8. Li Q, Liu Z (2009) Tongue color analysis and discrimination based on hyperspectral images. Comput Med Imaging Graph 33(3): 217–221

    Article  PubMed  Google Scholar 

  9. Snell RS (2003) Clinical anatomy, 7th edn. Lippincott Williams & Wilkins, Philadelphia

    Google Scholar 

  10. Kessel RG (1998) Basic medical histology: the biology of cells, tissues, and organs. 1. Oxford University Press, USA

    Google Scholar 

  11. Tsumura N, Haneishi H, Miyake Y (1999) Independent-component analysis of skin color image. J Opt Soc Am A 16(9): 2169–2176

    Article  CAS  Google Scholar 

  12. Terasawa K, Toriizuka K, Tosa H, Ueno M, Hayashi T, Shimizu M (1986) Rheological studies on ‘Oketsu’ syndrome: I. The blood viscosity and diagnostic criteria. J Med Pharmaceut Soc WAKAN-YAKU 3: 98–104

    Google Scholar 

  13. Terasawa K, Itoh T, Morimoto Y, Hiyama Y, Tosa H (1988) The characteristics of the microcirculation of bulbar conjunctiva in ‘Oketsu’ syndrome. J Med Pharmaceut Soc WAKAN-YAKU 5: 200–205

    Google Scholar 

  14. Pang B, Zhang D, Wang K (2005) Tongue image analysis for appendicitis diagnosis. Inform Sci 175(3): 160–176

    Article  Google Scholar 

  15. Zhang D, Pang B, Li N, Wang K, Zhang H (2005) Computerized diagnosis from tongue appearance using quantitative feature classification. Am J Chin Med 33(6): 859–866

    Article  PubMed  Google Scholar 

  16. Tsumura N, Ojima N, Sato K, Shiraishi M, Shimizu H, Nabeshima H, Akazaki S, Hori K, Miyake Y (2003) Image-based skin color and texture analysis/synthesis by extracting hemoglobin and melanin information in the skin. ACM Trans Graph 22(3): 770–779

    Article  Google Scholar 

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Correspondence to Satoshi Yamamoto.

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Yamamoto, S., Tsumura, N., Nakaguchi, T. et al. Principal component vector rotation of the tongue color spectrum to predict “Mibyou” (disease-oriented state). Int J CARS 6, 209–215 (2011). https://doi.org/10.1007/s11548-010-0506-8

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  • DOI: https://doi.org/10.1007/s11548-010-0506-8

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