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Related Experiment Video

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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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Computerized tongue image segmentation via the double geo-vector flow.

Miao-Jing Shi, Guo-Zheng Li1, Fu-Feng Li

  • 1Department of Control Science & Engineering, Tongji University, Shanghai, P,R,, China. gzli@tongji.edu.cn.

Chinese Medicine
|February 11, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a novel double geo-vector flow (DGF) method for accurate automatic tongue segmentation in traditional Chinese medicine (TCM) imaging. The DGF method demonstrates high efficiency and effectiveness in precisely identifying tongue regions.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Traditional Chinese Medicine

Background:

  • Tongue analysis is a diagnostic method in Traditional Chinese Medicine (TCM).
  • Variations in tongue features (color, texture, coating, shape) complicate precise region extraction in images.
  • Objective evaluation of TCM tongue diagnosis requires automated segmentation techniques.

Purpose of the Study:

  • To quantitatively evaluate automatic tongue segmentation for improved TCM diagnosis.
  • To develop and assess a novel algorithm for precise tongue image segmentation.

Main Methods:

  • Utilized a clinical tongue image dataset from Shanghai University of TCM.
  • Employed a saliency window for image refinement.
  • Proposed a double geo-vector flow (DGF) algorithm for tongue edge detection and segmentation, integrating geodesic and geo-gradient vector flow concepts.

Main Results:

  • The DGF method achieved high accuracy on 100 test images.
  • Demonstrated superior performance compared to existing methods with a true-positive volume fraction of 98.5%.
  • Reported low error rates: false-positive volume fraction of 1.51%, false-negative volume fraction of 1.42%, Hausdorff distance of 0.29%, and mean distance of 1.43%.

Conclusions:

  • The DGF algorithm is efficient and effective for automatic tongue image segmentation.
  • Performance evaluation using standard boundary and area error metrics confirms its utility.
  • This automated approach supports quantitative analysis in TCM tongue diagnosis.