Automated contour detection in spine radiographs and computed tomography reconstructions for forensic comparative identification
Résumé
This study was conducted to test an automated method to identify unknown individuals. It relies on a previous
radiographic file and uses an edge-based comparison of lumbar CT/PMCT reconstructions and radiographs. The
living group was composed of 15 clinical lumbar spine CT scans and 15 paired radiographs belonging to the same
patients. The deceased group consisted of 5 lumbar spine PMCT scans and 5 paired antemortem radiographs of
deceased individuals plus the 15 unpaired radiographs belonging to the living. An automated method using image
filtering (anisotropic diffusion) and edge detection (Canny filter) provided image contours. Cross comparisons of all
the exams in each group were performed using similarity measurements under the affine registration hypothesis. The
Dice coefficient and Hausdorff distance values were significantly linked (p< 0.001 and p= 0.001 respectively) to the
matched examinations in the living group (p < 0.001; pseudo-R2 = 0.70). 12 of the 15 examinations were correctly
paired, 2 were wrongly paired and 3 were not paired when they must have been. In the deceased group, the
Hausdorff distance was significantly linked (p= 0.018) to the matched examinations (p< 0.001; pseudo-R2 = 0.62;
Dice coefficient p= 0.138). The paired examinations were all correctly found, but one was wrongly paired. The
negative predictive value was above 98% for both groups. We highlighted the feasibility of comparative radiological
identification using automated edge detection in cross-modality (CT/PMCT scan and radiographs) examinations. This
method could be of significant help to a radiologist or coroner in identifying unknown cadavers.