Next‐generation osteometric sorting: Using 3D shape, elliptical Fourier analysis, and Hausdorff distance to optimize osteological pair‐matching

Abstract

Determining which bilateral bones belong to the same person based on shape and size similarity is called pair‐matching and it is instrumental for sorting commingled skeletons. To date, pair‐matching has popularly been accomplished by visual inspection and/or linear caliper measurements; however, attention is turning increasingly to computational analysis. In this paper, we investigate a fast three‐dimensional (3D) computerized shape‐analysis method for whole‐bone pair‐matching using a test sample of 14 individuals (23 femora, 26 humeri, and 26 tibiae). Specifically, the method aims to find bilateral pairs using, as the shape signature criterion, a single 3D outline that snakes around each bone s perimeter as described by a 3D elliptical Fourier analysis function. This permits substantial 3D‐point‐cloud data reduction, that is, to 0.02% of the starting c.500,000 point cloud or just 100 points, while preserving key 3D shape information. The mean Hausdorff distance (Hd) was applied to measure the distance between each mirrored right‐side outline to every left‐side outline in pairwise fashion (132, 168 and 169 comparisons, respectively). Both thresholds and lowest Hd were investigated as pair‐match criteria, with the lowest Hd producing the best performance results for searches jointly utilizing right‐left and left‐right directions for comparison: true positive rates of 1.00 (10/10), 1.00 (12/12), and 0.92 (11/12) for the femora, humeri, and tibiae, respectively. The computational time to calculate 469 pairwise 3D comparisons on a single stock‐standard Intel® Core™ i7‐4650U CPU @ 1.70 GHz was 5 s. This short data processing time makes the method viable for real‐world application.