An Automated Two-Dimensional Form Registration Method for Osteological Pair-Matching

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

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.

Three-Dimensional Geometry of Phalanges as a Proxy for Pair-Matching: Mesh Comparison Using an ICP Algorithm

Forensic anthropologists are frequently faced with the challenge of individualizing and sorting commingled remains in a variety of scenarios. A number of protocols have been proposed to standardize the methodological approach to individuating commingled remains, some of which are focused on pair-matching. A recent study by Karell et al. (2016) proposed a virtual method for pair-matching humeri using a semi-automatic procedure that gave encouraging results. With regards to the phalanges, there are only a handful of studies focusing on identifying and siding phalanges, as well as exploring their directional and functional asymmetry. Yet, they are still as important as every other bone when sorting commingled human remains in various situations, such as archaeological common burials and mass graves, commingled decomposed remains resulting from atrocities, accidents or natural disasters. This study investigates a new method for pair-matching, a common individualization technique, using digital three-dimensional models of bone: mesh-to-mesh value comparison (MVC) as proposed by Karell et al. (2016). The MVC method digitally compares the entire three-dimensional geometry of two bones using an iterative closest point (ICP) algorithm to produce a single value as a proxy for their similarity. The method is automated with the use of Viewbox software 4.1 beta for a simultaneous comparison of all possible pairs. For this study, 515 phalanges from 24 individuals of mixed ancestry were digitized using CT scans and the 3D modeling program AMIRA 5.3.3. The models were also hollowed (internal information of compact and trabecular bone removed) to test the method with simulated surface scan models. The subsequent data-over 73,000 comparisons-were assessed using sensitivity and specificity rates via ROC analysis to indicate how well the automated version of MVC pair-matched phalanges. The best bone in terms of pair-matching was the proximal phalanx of Digit 3 with 87.5% sensitivity and 92.4% specificity rates at a threshold value of 0.488 for the unhollowed bones. The specificity drops slightly (91.1%) when the hollowed models are compared. To compare the performance of the method in all phalanges, the specificity was set to 95%-allowing for a 5% acceptable error-and the adjusted sensitivity was compared. The highest sensitivity, namely 68.8%, was noted for Digit 2 proximal phalanx for both unhollowed and hollowed models. Thus far, our preliminary results indicate that the MVC method performs well when pair-matching phalanges, though it is less accurate than pair-matching other types of bones. The introduction of 95% specificity threshold allows for rejecting pairs in great confidence, which could, for instance, significantly reduce the number of DNA comparisons required for the remaining possible matches. In addition, the similar results obtained from hollowed and unhollowed models indicate that the internal information included in the unhollowed models adds little to the identification of true pairs. This means that if a CT scan is not available, the method could be applied to surface models produced by light and laser scanners as well. While additional work needs to be done to verify these preliminary results, this research has the potential to expand the repertoire of individualization methods.

An Automated Two-dimensional Pairwise form Registration Method for Pair-matching of Fragmented Skeletal Remains

This study introduces an automated pairwise method for osteological pair-matching of fragmented skeletal remains using two-dimensional fragmented outlines extracted from photographs. The form data are used in pairwise iterative closest point registrations with rigid transformations. A modified version of the average Hausdorff distance is calculated to remove any coordinate correspondences with outline fracture margins, which allow the distance analysis of fragmented outlines. A dilation modification to the Hausdorff distance is proposed creating a greater separation between true- and false-pairs. The sample consists of 122 calcanei (61 pairs) from the UI-Stanford collection. Performance statistics are provided for simulated fragmented and complete assemblages. Results indicate up to 98% accuracy for fragmented and complete assemblages. The dilated Hausdorff distance performed similarly across assemblages, but showed a slight decrease in performance for the complete assemblage. This approach provides a useful short listing tool to reduce the number of visual comparisons required in large commingled assemblages.