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Quantitative Visualization and Detection of Skin Cancer Using Dynamic Thermal Imaging
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Quantitative evaluation of elderly skin based on digital image analysis.

Hiromasa Tanaka1, Gojiro Nakagami, Hiromi Sanada

  • 1Department of Gerontological Nursing/Wound Care Management, Graduate School of Medicine, University of Tokyo, Tokyo, Japan. hiromasat-tky@umin.ac.jp

Skin Research and Technology : Official Journal of International Society for Bioengineering and the Skin (ISBS) [And] International Society for Digital Imaging of Skin (ISDIS) [And] International Society for Skin Imaging (ISSI)
|April 17, 2008
PubMed
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Objective skin health assessment for the elderly is now possible using digital images. This study developed a reliable method to analyze skin texture, identifying key indices for evaluating skin integrity and improving clinical assessments.

Area of Science:

  • Dermatology
  • Medical Imaging
  • Gerontology

Background:

  • Clinical skin state evaluation relies on subjective clinician assessment, which can be inconsistent.
  • This subjectivity poses challenges in accurately determining skin health, especially in elderly individuals predisposed to disease.

Purpose of the Study:

  • To develop a novel, objective method for evaluating elderly skin integrity using digital imaging.
  • To establish a reliable 'gold standard' for clinical evaluation of elderly skin images by specialists.
  • To identify image analysis indices that correlate with expert clinical assessments of skin condition.

Main Methods:

  • Collected and analyzed 208 digital skin images from 34 elderly subjects.
  • Employed modified Kobayashi's method and texture analysis for image processing and feature extraction.

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  • Six plastic surgeons rated skin texture regularity (1-10 scale), with inter-rater reliability confirmed via intraclass correlation coefficients (ICC).
  • Stepwise multiple regression analysis correlated image-derived indices with the specialist-derived 'gold standard' evaluation.

    • Main Results:

    • High inter-rater reliability (ICC=0.92) supported the use of a combined specialist score.
    • Stepwise regression identified interval (L), thickness (T(ave)), energy (0 degrees), and entropy (45 degrees) as significant predictors of clinical evaluation.
    • The developed model explained 59% (R(2)=0.59) of the variance in clinician assessments.

    Conclusions:

    • Quantitative evaluation of digital skin images offers a viable method for assessing elderly skin integrity.
    • Key image analysis indices were identified that effectively explain specialist clinical evaluations.
    • This approach provides a simple, image-based tool for objective skin health assessment in the elderly.