Comparison of automated post-processing techniques for measurement of body surface area from 3D photonic scans

Body surface area (BSA) measurement is important in engineering and medicine fields to determine parameters for various applications. Three-dimensional scanning techniques may be used to acquire the BSA directly. Nevertheless, the raw data obtained from 3D scanning usually requires some manual post-processing which is time-consuming and requires technical expertise. Automated post-processing of 3D scans enables expedient BSA calculation with minimal technical expertise. The purpose of this research was to compare the accuracy and reliability of three different automated post-processing techniques including Stitched Puppet (SP), Poisson surface reconstruction (PSR), and screened Poisson surface reconstruction (SPSR) using manual post-processing as the criterion. Twenty-nine participants were scanned twice, and raw data were processed with the manual operation and automated techniques to acquire BSAs separately. The reliability of BSAs acquired from these approaches was represented by the relative technical error of measurements (TEM). Pearson’s regressions were applied to correct BSAs acquired from the automated techniques. The limits of agreement (LOA) were used to quantify the accuracy of BSAs acquired from the automated techniques and corrected by regression models. The reliability (relative TEM) of BSAs obtained from PSR, SPSR and SP were 0.32, 0.30 and 0.82% respectively. After removing bias with the regression models, the LOA for PSR, SPSR and SP were (−0.0134 m², 0.0135 m²), (−0.0130 m², 0.0132 m²), (−0.0573 m², 0.0572 m²) respectively. It is concluded that PSR and SPSR are good alternative approaches to manual post-processing for applications that need reliable and accurate measurements of BSAs with large populations.