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

Abstract

We present our latest work on in vivo skin capacitive imaging analysis by using grey level co-occurrence matrix (GLCM). The in vivo skin capacitive images were taken by a capacitance based fingerprint sensor, the skin capacitive images were then analysed by GLCM. Four different GLCM feature vectors, angular second moment (ASM), entropy (ENT), contrast (CON) and correlation (COR), are selected to describe the skin texture. The results show that angular second moment increases as age increases, and entropy decreases as age increases. The results also suggest that the angular second moment values and the entropy values reflect more about the skin texture, whilst the contrast values and the correlation values reflect more about the topically applied solvents. The overall results shows that the GLCM is an effective way to extract and analyse the skin texture information, which can potentially be a valuable reference for evaluating effects of medical and cosmetic treatments.