Advertisement

Tongue Image Analysis and Its Mobile App Development for Health Diagnosis

  • Ratchadaporn Kanawong
  • Tayo Obafemi-Ajayi
  • Dahai Liu
  • Meng Zhang
  • Dong Xu
  • Ye DuanEmail author
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1005)

Abstract

Computer-aided diagnosis provides a medical procedure that assists physicians in interpretation of medical images. This work focuses on computer-aided tongue image analysis specifically, based on Traditional Chinese Medicine (TCM). Tongue diagnosis is an important component of TCM. Computerized tongue diagnosis can aid medical practitioners in capturing quantitative features to improve reliability and consistency of diagnosis. Recently, researchers have started to develop computer-aided tongue analysis algorithms based on new advancement in digital photogrammetry, image analysis, and pattern recognition technologies. In this chapter, we will describe our recent work on tongue image analysis as well as a mobile app that we developed based on this technology.

Keywords

Computer-aided diagnosis Tongue image analysis Traditional Chinese Medicine Mobile app 

References

  1. 1.
    Ma T, Tan C, Zhang H, Wang M, Ding W, Li S. Bridging the gap between traditional Chinese medicine and systems biology: the connection of cold syndrome and NEI network. Mol BioSyst. 2010;6:613–9.CrossRefPubMedGoogle Scholar
  2. 2.
    Kanawong R, Xu W, Xu D, Li S, Ma T, Duan Y. An automatic tongue detection and segmentation framework for computer-aided tongue image analysis., Int J Funct Inform Pers Med, vol. 4; 2011. p. 56.Google Scholar
  3. 3.
    Li S, Zhang ZQ, Wu LJ, Zhang XG, Li YD, Wang YY. Understanding ZHENG in traditional Chinese medicine in the context of neuro-endocrine-immune network. IET Syst Biol. 2007;1(1):51–60.CrossRefPubMedGoogle Scholar
  4. 4.
    Li S. Network systems underlying traditional Chinese medicine syndrome and herb formula. Curr Bioinforma. 2009;4:188–96.CrossRefGoogle Scholar
  5. 5.
    Chiu CC, Lin HS, Lin SL. A structural texture recognition approach for medical diagnosis through tongue. Biomed Eng Appl Basis Commun. 1995;7(2):143–8.Google Scholar
  6. 6.
    Wang YG, Yang J, Zhou Y, Wang YZ. Region partition and feature matching based color recognition of tongue image. Pattern Recogn Lett. 2007;28(1):11–9.CrossRefGoogle Scholar
  7. 7.
    Li CH, Yuen PC. Tongue image matching using color content. Pattern Recogn. 2002;35(2):407–19.CrossRefGoogle Scholar
  8. 8.
    Liu Z, Yan JQ, Zhang D, Li QL. Automated tongue segmentation in hyperspectral images for medicine. Applied Optic. 2007;46(34):8328–34.CrossRefGoogle Scholar
  9. 9.
    Zhang BP, Wang DK. The bi-elliptical deformable contour and its application to automated tongue segmentation in Chinese medicine. IEEE Trans Med Imaging. Aug. 2005;24(8):946–56.CrossRefPubMedGoogle Scholar
  10. 10.
    Zhang D, Liu Z, Yan JQ. Dynamic tongueprint: a novel biometric identifier. Pattern Recogn. 2010;43(3):1071–82.CrossRefGoogle Scholar
  11. 11.
    Chiu CC. A novel approach based on computerized image analysis for traditional Chinese medical diagnosis of the tongue. Comp Methods Prog Biomed. 2000;61:77–89.CrossRefGoogle Scholar
  12. 12.
    Chiu CC. The development of a computerized tongue diagnosis system. Biomed Eng Appl Basis Commun. 1996;8(4):342–50.Google Scholar
  13. 13.
    Horng CH. The principles and methods of tongue diagnosis. In: Tongue diagnosis. Taipei: Lead Press; 1993.Google Scholar
  14. 14.
    Freund Y, Schapire RE. A decision theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci. 1997;55(1):119–39.CrossRefGoogle Scholar
  15. 15.
    Burges CJ. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc. 1998;2:121–67.CrossRefGoogle Scholar
  16. 16.
    Platt J. Sequential minimal optimization: a fast algorithm for training support vector machines. In: Scholkopf B, Burges C, Smola A, editors. Advances in kernel methods – support vector learning. Cambridge, MA: MIT Press; 1998.Google Scholar
  17. 17.
    Alpaydin E. Introduction to machine learning. Cambridge, MA: MIT Press; 2004.Google Scholar
  18. 18.
    Bouzerdoum A, Havstad A, Beghdadi A, Image quality assessment using a neural network approach. In: Fourth IEEE International symposium on signal processing and information technology, 2004.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  • Ratchadaporn Kanawong
    • 1
  • Tayo Obafemi-Ajayi
    • 2
  • Dahai Liu
    • 3
  • Meng Zhang
    • 3
  • Dong Xu
    • 3
  • Ye Duan
    • 3
    Email author
  1. 1.Silapakorn UniversityBangkokThailand
  2. 2.Missouri State UniversitySpringfieldUSA
  3. 3.Department of Electrical Engineering & Computer Science and MU Informatics InstituteUniversity of MissouriColumbiaUSA

Personalised recommendations