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In vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM).

Xiang Ou1, Wei Pan1, Perry Xiao1

  • 1Photophysics Research Centre, London South Bank University, 103 Borough Road, London SE1 0AA, UK.

International Journal of Pharmaceutics
|November 6, 2013
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Summary
This summary is machine-generated.

Grey Level Co-occurrence Matrix (GLCM) analysis of in vivo skin capacitive images reveals age-related texture changes. Angular second moment and entropy effectively quantify skin texture, offering insights for cosmetic and medical treatments.

Keywords:
Capacitive imagingFeature vectorsGrey level co-occurrence matrixSkin textureSolvent penetrationTrans-dermal drug delivery

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Area of Science:

  • Biomedical Engineering
  • Dermatology
  • Image Analysis

Background:

  • Assessing in vivo skin properties is crucial for evaluating treatments.
  • Traditional methods may not capture subtle textural changes.
  • Capacitive imaging offers a non-invasive approach to skin analysis.

Purpose of the Study:

  • To analyze in vivo skin capacitive images using the Grey Level Co-occurrence Matrix (GLCM).
  • To identify quantifiable texture features related to skin aging and topical applications.
  • To establish GLCM as a method for evaluating skin treatment efficacy.

Main Methods:

  • Acquisition of in vivo skin capacitive images using a capacitance-based fingerprint sensor.
  • Analysis of image texture using the Grey Level Co-occurrence Matrix (GLCM).
  • Extraction and evaluation of four GLCM feature vectors: Angular Second Moment (ASM), Entropy (ENT), Contrast (CON), and Correlation (COR).

Main Results:

  • Angular Second Moment (ASM) positively correlates with increasing age.
  • Entropy (ENT) shows a negative correlation with increasing age.
  • ASM and ENT values primarily reflect intrinsic skin texture.
  • CON and COR values are more sensitive to the effects of topically applied solvents.

Conclusions:

  • GLCM is an effective technique for extracting and analyzing skin texture information.
  • Age-related changes in skin texture can be quantified using GLCM features like ASM and ENT.
  • GLCM analysis holds potential as a valuable tool for assessing the impact of medical and cosmetic treatments on skin.