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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

An optimized tongue image color correction scheme.

Xingzheng Wang1, David Zhang

  • 1Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong. csxzwang@comp.polyu.edu.hk

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|September 16, 2010
PubMed
Summary
This summary is machine-generated.

Digital cameras produce device-dependent color images, impacting computer-aided tongue analysis. This study introduces a correction scheme to standardize tongue images into a device-independent color space, improving accuracy and consistency.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Color Science

Background:

  • Digital camera images present color information in device-dependent RGB color spaces, which vary based on camera characteristics.
  • This variability poses a significant challenge for computer-aided tongue image analysis, which requires accurate and consistent color representation.
  • Accurate color rendering is crucial for reliable diagnosis and analysis based on tongue imagery.

Purpose of the Study:

  • To develop and validate an optimized correction scheme for standardizing device-dependent tongue images.
  • To transform tongue images from various device-dependent color spaces into a unified, device-independent color space.
  • To enhance the reliability and consistency of color information in computer-aided tongue image analysis.

Main Methods:

  • Proposed an optimized correction scheme utilizing regression and neural network algorithms.
  • Compared polynomial-based regression, ridge regression, support vector regression, and neural network mapping for algorithm generation.
  • Evaluated the scheme's performance using CIE L(*)a(*)b(*) color difference (∆E(ab)(*)) on a colorchecker and real tongue images.

Main Results:

  • The correction scheme achieved a color difference of less than 5 (∆E(ab)(*) < 5) on a standard colorchecker.
  • Experimental results demonstrated a significant reduction in color variation among tongue images captured under different device conditions.
  • The average color difference between distorted tongue images was reduced by over 95% after applying the correction scheme.

Conclusions:

  • The proposed optimized correction scheme effectively converts device-dependent tongue images to a device-independent color space.
  • This standardization significantly improves the consistency and comparability of tongue images for computer-aided analysis.
  • The method offers a robust solution for overcoming device-specific color distortions in medical imaging applications.