Three-dimensional surface scanning methods in osteology: A topographical and geometric morphometric comparison

Objectives Three-dimensional (3D) data collected by structured light scanners, photogrammetry, and computed tomography (CT) scans are increasingly combined in joint analyses, even though the scanning techniques and reconstruction software differ considerably. The aim of the present study was to compare the quality and accuracy of surface models and landmark data obtained from modern clinical CT scanning, 3D structured light scanner, photogrammetry, and MicroScribe digitizer. Material and methods We tested 13 different photogrammetric software tools and compared surface models obtained by different methods for four articulated human pelves in a topographical analysis. We also measured a set of 219 landmarks and semilandmarks twice on every surface as well as directly on the dry bones with a MicroScribe digitizer. Results Only one photogrammetric software package yielded surface models of the complete pelves that could be used for further analysis. Despite the complex pelvic anatomy, all three methods (CT scanning, 3D structured light scanning, photogrammetry) yielded similar surface representations with average deviations among the surface models between 100 and 200 mu m. A geometric morphometric analysis of the measured landmarks showed that the different scanning methods yielded similar shape variables, but data acquisition via MicroScribe digitizer was most prone to error. Discussion We demonstrated that three-dimensional models obtained by different methods can be combined in a single analysis. Photogrammetry proved to be a cheap, quick, and accurate method to generate 3D surface models at useful resolutions, but photogrammetry software packages differ enormously in quality.

Automated summary

This study compared the quality and accuracy of surface models and landmark data obtained from different scanning methods, showing that despite the differences in techniques, the surface models obtained were similar in accuracy. However, data acquisition via MicroScribe digitizer was found to be the most error-prone method.