Cadaver dogs--a study on detection of contaminated carpet squares

INTRODUCTION: Cadaver dogs are known as valuable forensic tools in crime scene investigations. Scientific research attempting to verify their value is largely lacking, specifically for scents associated with the early postmortem interval. The aim of our investigation was the comparative evaluation of the reliability, accuracy, and specificity of three cadaver dogs belonging to the Hamburg State Police in the detection of scents during the early postmortem interval. MATERIAL AND METHODS: Carpet squares were used as an odor transporting media after they had been contaminated with the scent of two recently deceased bodies (PMI<3h). The contamination occurred for 2 min as well as 10 min without any direct contact between the carpet and the corpse. Comparative searches by the dogs were performed over a time period of 65 days (10 min contamination) and 35 days (2 min contamination). RESULTS: The results of this study indicate that the well-trained cadaver dog is an outstanding tool for crime scene investigation displaying excellent sensitivity (75-100), specificity (91-100), and having a positive predictive value (90-100), negative predictive value (90-100) as well as accuracy (92-100).

Comparison of partial least squares regression and principal component regression for pelvic shape prediction.

This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures were used in a cross-validation study to predict the pelvic shape. Results obtained from applying these two different regression techniques were compared to the population mean model. In almost all the prediction experiments, both regression techniques unanimously generated better results than the population mean model, while the difference on prediction accuracy between these two regression methods is not statistically significant (α=0.01).

3D volumetric intensity reconsturction from 2D x-ray images using partial least squares regression

Reconstruction of shape and intensity from 2D x-ray images has drawn more and more attentions. Previously introduced work suffers from the long computing time due to its iterative optimization characteristics and the requirement of generating digitally reconstructed radiographs within each iteration. In this paper, we propose a novel method which uses a patient-specific 3D surface model reconstructed from 2D x-ray images as a surrogate to get a patient-specific volumetric intensity reconstruction via partial least squares regression. No DRR generation is needed. The method was validated on 20 cadaveric proximal femurs by performing a leave-one-out study. Qualitative and quantitative results demonstrated the efficacy of the present method. Compared to the existing work, the present method has the advantage of much shorter computing time and can be applied to both DXA images as well as conventional x-ray images, which may hold the potentials to be applied to clinical routine task such as total hip arthroplasty (THA).