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

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.