Sample area for surface roughness determination of skin surfaces

A surface roughness algorithm has been developed and validated for determining roughness of psoriasis lesions. The algorithm extracts an estimated waviness surface from 3D rough surface of psoriasis lesion by applying high order polynomial surface fitting. Vertical deviations of the lesion are determined by subtracting its 3D surface from the estimated waviness surface. However, the performance of the algorithm is dependent on the area of skin surface. The objective of this paper is to determine the minimum area for optimal performance of the skin surface roughness algorithm. In the determined sample area, all significant roughness components must be covered for surface roughness determination. To find the minimum size of sampled area, skin surface roughness has been determined at several sampling area variations. Normal skin surfaces are used as input data in this evaluation. By referring to the plot of surface roughness dependency on sampled area variation, it can be shown that the threshold area is found to be 4.9×4.9 mm² for skin surface roughness stability. Skin surface roughness variation is less for the sample areas larger than this threshold. However, there is a small surface roughness increment after the surface roughness stability. It is caused by fitting error at border regions of very large sample size.