Study on Digitization of TCM Diagnosis Applied Extraction Method of Blood Vessel

DOI: 10.4236/jsip.2011.24043   PDF   HTML     5,172 Downloads   7,665 Views   Citations


This paper presents a study on digitization of Traditional Chinese Medicine diagnosis. The research consists of two aspects: a) algorithms for blood vessels extraction in sclera-conjunctiva images, which can be applied in syndrome differentiation by observing human eyes (named Ocular Diagnostic in Traditional Chinese Medicine); b) digitization of extracted vessels. First, sclera-conjunctiva region is isolated by optimal threshold segmentation and mathematical operation; Scanning and edge detection methods are used to gain the edge of the blood vessels. Moreover, the edge feature parameters are gained, which can be used to reconstruct the blood vessels. Experimental results show that blood vessels information can be obtained fast and accurately for the further TCM diagnosis by artificial system.

Share and Cite:

C. Wu and K. Harada, "Study on Digitization of TCM Diagnosis Applied Extraction Method of Blood Vessel," Journal of Signal and Information Processing, Vol. 2 No. 4, 2011, pp. 301-307. doi: 10.4236/jsip.2011.24043.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] J. J. Wang, “The Theory of Differentiation of Syndromes by Observing Eyes in Traditional Chinese Medicine,” Chinese Journal of Basic Medicine in Traditional Chinese Medicine, Vol. 11, No. 5, 2005, pp. 324-325.
[2] D. L. Zheng and Z. F. Zheng, “Ocular Diagnostic-Understand Fully at a Glance and Diagnose Patient’s Disease,” Liaoning Science and Technology Publishing House, Liaoning, 2003.
[3] D. L. Zheng, “Complete Guide to Visualized Diagnostics via Eye Study,” Liaoning Science and Technology Publishing House, Liaoning, 2008.
[4] G. D. Zhu, “Research on the Digitalization Technology of ‘Differentiation of Syndromes by Observing Eyes’,” Doctoral Dissertation, Graduate University of Chinese Academy of Sciences, 2006.
[5] S. Paripurana, W. Chiracharit, K. Chamnongthai and K. Higuchi, “Retinal Blood Vessel Segmentation Based on Fractal Dimension in Spatial-Frequency Domain”, 10th International Symposium on Communications and Information Technologies, Tokyo, 26-29 October 2010, pp. 1185-1190.
[6] G. D. Zhu, L. Shen and J. J. Wang, “Automatic Vessel Extraction for Sclera-Conjunctiva Images Based on Exploratory Tracking,” Computer Engineering, Vol. 31, No. 17, 2005, pp. 6-8.
[7] R. C. Gonzalez, R. E. Woods and S. L. Eddins, “Digital Image Processing Using MATLAB,” Prentice Hall, Upper Saddle River, 2003.
[8] J. Canny, “A Computational Approach for Edge Detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 8, No. 6, 1986, pp.679-698. doi:10.1109/TPAMI.1986.4767851
[9] R. C. Gonzalez and R. E. Woods, “Digital Image Processing,” 2nd Edition, Prentice Hall, Upper Saddle River, 2002.
[10] A. D. Brinka, “Thresholding of Digital Images Using Two-Dimensional Entropies,” Pattern Recognition, Vol. 25, No. 8, 1992, pp. 803-808. doi:10.1016/0031-3203(92)90034-G
[11] N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 9, 1979, pp. 62-66.
[12] Y. Sun, “Automatic Identification of Vessel Contours in Coronary Arteriograms by an Adaptive Tracking Algorithm,” IEEE Transactions on Medical Imaging, Vol. 8, No. 1, 1989, pp. 78-88.

comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.