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Evidence-Based Complementary and Alternative Medicine
Volume 2015, Article ID 483805, 12 pages
http://dx.doi.org/10.1155/2015/483805
Research Article

Partially Automated Method for Localizing Standardized Acupuncture Points on the Heads of Digital Human Models

1Nano Primo Research Center, Advanced Institutes of Convergence Technology, Seoul National University, Suwon 443-270, Republic of Korea
2Pharmacopuncture Medical Research Center, Korean Pharmacopuncture Institute, Seoul 157-801, Republic of Korea

Received 27 February 2015; Revised 6 May 2015; Accepted 13 May 2015

Academic Editor: Vitaly Napadow

Copyright © 2015 Jungdae Kim and Dae-In Kang. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Having modernized imaging tools for precise positioning of acupuncture points over the human body where the traditional therapeutic method is applied is essential. For that reason, we suggest a more systematic positioning method that uses X-ray computer tomographic images to precisely position acupoints. Digital Korean human data were obtained to construct three-dimensional head-skin and skull surface models of six individuals. Depending on the method used to pinpoint the positions of the acupoints, every acupoint was classified into one of three types: anatomical points, proportional points, and morphological points. A computational algorithm and procedure were developed for partial automation of the positioning. The anatomical points were selected by using the structural characteristics of the skin surface and skull. The proportional points were calculated from the positions of the anatomical points. The morphological points were also calculated by using some control points related to the connections between the source and the target models. All the acupoints on the heads of the six individual were displayed on three-dimensional computer graphical image models. This method may be helpful for developing more accurate experimental designs and for providing more quantitative volumetric methods for performing analyses in acupuncture-related research.