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Development of Tongue Diagnosis System Using ASM and SVM

ASM과 SVM을 이용한 설진 시스템 개발

  • 박진웅 (원광대학교 컴퓨터공학과) ;
  • 강선경 (원광대학교 컴퓨터공학과) ;
  • 김영운 (원광대학교 컴퓨터공학과) ;
  • 정성태 (원광대학교 컴퓨터공학과)
  • Received : 2013.01.01
  • Accepted : 2013.03.25
  • Published : 2013.04.30

Abstract

In this study, we propose a tongue diagnosis system which detects the tongue from face image and divides the tongue area into six areas, and finally generates tongue fur ratio of each area. To detect the tongue area from face image, we use ASM as one of the active shape models. Detected tongue area is divided into six areas and the distribution of tongue coating of six areas is examined by SVM. For SVM, we use a 3-dimensional vector calculated by PCA from a 12-dimensional vector consisting of RGB, HSV, Lab, and Luv. As a result, we stably detected the tongue area using ASM. Furthermore, we recognized that PCA and SVM helped to raise the ratio of tongue coating detection.

본 논문에서는 설진을 위하여 얼굴 영상으로부터 혀 영역을 추출하고, 혀 영역을 6개 세부 영역으로 분할한 다음 영역별 설태 비율을 검출하는 방법을 제안한다. 얼굴 영상으로부터 혀 영역을 추출하기 위해 능동적 형태 모델방법의 하나인 ASM을 이용하였다. 검출된 혀 영역을 한의학에서 사용하는 일반적인 6개 영역으로 분할하였고, 분할된 영역 내에서의 설태 분포 정도를 SVM을 이용하여 검출하였다. SVM 분류 시 특징 벡터로는 RGB, HSV, Lab, Luv로 구성된 12차원의 벡터로부터 주성분 분석을 통하여 구해진 3차원의 벡터를 사용하였다. 실험 결과 ASM을 사용하여 혀 영역을 안정적으로 검출할 수 있었고 주성분 분석과 SVM을 활용함으로써 설태 검출율이 높아짐을 알 수 있었다.

Keywords

References

  1. L. Sun, Z. Cheng and H. Xie, "Study on objective tongue diagnosis using computerized Image recognition technique", Journal of Anhui Traditional Chinese Medical College, Vol.5, No.4, pp. 5-7, 1989.
  2. X.Q. Yue and Q. Liu, "Analysis of studies on pattern recognition of tongue image in traditional Chinese medicine by computer technology", Journal of Chinese Medicine, Vol.2, No.5, pp. 326-329, 2004.
  3. B. Pang and D. Zhang, "Computerized tongue diagnosis based on bayesian networks", IEEE Transaction on Biomedical Engineering, Vol.51, No.10, pp. 1803-1810, 2004 https://doi.org/10.1109/TBME.2004.831534
  4. H.Z. Zhang, K.Q. Wang, D. Zhang, B. Pang and B. Huang, "Computer aided tongue diagnosis system", Proceedings of the 2005 IEEE Engineering in Medicine and Biology, pp. 6754-6757, 2005.
  5. L. Zhi et. al., "Classification of hyperspectral medical tongue images for tongue diagnosis", Computational Medicine Imaging, Vol.31, No.8, pp. 672-678, 2007. https://doi.org/10.1016/j.compmedimag.2007.07.008
  6. J. Lee, E.J. Choi, H.H. Ryu, H.J Lee, Y.J. Lee, K.M. Park, J.Y. Kim, "Design of Discriminant Fuction for White and Tellow Coating with Multi-dimensional Color Vectors", Korean Journal of Oriental Medicine, Vol. 13, No. 2, pp. 47-52, 2007.
  7. K.H. Kim, J. Lee, E.J Choi, H.H. Ryu, J.Y. Kim,"Extraction of Tongue Region using Graph and Geometric Information", Transaction of KIEE, Vol. 56, No. 11, pp. 2051-2057, 2007.
  8. Y.S. Hong, "Smart Tongue Electronic Chart System", The journal of the Institute of Webcasting, Internet and Telecommunication, Vol. 12, No. 2, pp. 243-249, 2012. https://doi.org/10.7236/JIWIT.2012.12.2.243
  9. J.H. Kim and D.H. Nam, "Colour Interpolation of Tongue Image in Digital Tongue Image System Blocking Out External Light", The journal of the Korea institute of oriental medical diagnostics, Vol. 16 No. 1, pp. 9-18, 2012
  10. C.Y. Choi, W.B. Lee, Y.S. Hong, D.H. Nam, and S.S. Lee, "Coated Tongue Region Extraction using the Fluorescence Response of the Tongue Coating by Ultraviolet Light Source", The journal of the Institute of Webcasting, Internet and Telecommunication, Vol. 12, No. 4, pp. 181-188, 2012. https://doi.org/10.7236/JIWIT.2012.12.4.181
  11. J. Kim, Y. Jung, K. Park, J.W. Park, "A digital tongue imaging system for tongue coating evaluation in patients with oral malodour", Oral Diseases, Vol. 15, No. 8, pp. 565-569, 2009. https://doi.org/10.1111/j.1601-0825.2009.01592.x
  12. K.H. Kim, J.H. Do, H.H. Ryu, J.Y. Kim, "Development of System Configuration and Diagnosis Methods for Tongue Diagnosis Instrument", Korean Journal of Oriental Medicine, Vol.14, No.3, pp. 89-95, 2008.
  13. T.F. Cootes, C.J. taylor, D.H. Cooper and J. Graham, "Active Shape Models - Their training and application", Journal of Computer Vision and Image Understating, Vol. 61, No. 1, pp. 38-59, 1995. https://doi.org/10.1006/cviu.1995.1004
  14. P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features", Proceedings of Computer Vision and Pattern Recognition, pp. I-511-I-518, 2001.
  15. Y. Freund and R.E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting", Proceeding of Computational Learning Theory, pp. 23-37, 1995.
  16. V.Vapnik,"A Tutorial on Support Vector Machines for Pattern Recognition", In Data Mining and Knowledge Discovery, Vol. 2, No. 2, pp. 121-167, 1998. https://doi.org/10.1023/A:1009715923555