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CHIU, Chuang-Yuan, PEASE, David L., FAWKNER, Samantha and SANDERS, Ross H. (2018). Automated body volume acquisitions from 3D structured-light scanning. Computers in Biology and Medicine, 101, 112-119.
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Abstract
Whole-body volumes and segmental volumes are highly related to the health and medical condition of individuals. However, the traditional manual post-processing of raw 3D scanned data is time-consuming and needs technical expertise. The purpose of this study was to develop bespoke software for obtaining whole-body volumes and segmental volumes from raw 3D scanned data automatically and to establish its accuracy and reliability. The bespoke software applied Stitched Puppet model fitting techniques to deform template models to fit the 3D raw scanned data to identify the segmental endpoints and determine their locations. Finally, the bespoke software used the location information of segmental endpoints to set segmental boundaries on the reconstructed meshes and to calculate body volume. The whole-body volumes and segmental volumes (head & neck, torso, arms, and legs) of 29 participants processed by the traditional manual operation were regarded as the references and compared to the measurements obtained with the bespoke software using the intra-method and inter-method relative technical errors of measurement. The results showed that the errors in whole-body volumes and most segmental volumes acquired from the bespoke software were less than 5%. Overall, the bespoke software developed in this study can complete the post-processing tasks without any technical expertise, and the obtained whole-body volumes and segmental volumes can achieve good accuracy for some applications in health and medicine.
| Item Type: | Article |
|---|---|
| Research Institute, Centre or Group - Does NOT include content added after October 2018: | Centre for Sports Engineering Research |
| Identification Number: | https://doi.org/10.1016/j.compbiomed.2018.07.016 |
| Page Range: | 112-119 |
| Depositing User: | Jill Hazard |
| Date Deposited: | 23 Aug 2018 15:41 |
| Last Modified: | 18 Mar 2021 03:42 |
| URI: | https://shura.shu.ac.uk/id/eprint/22363 |
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