Quantitative Differentiation of Bloodstain Patterns Resulting from Gunshot and Blunt Force Impacts

Bloodstain pattern analysis (BPA) provides significant evidentiary value in crime scene interpretation and reconstruction. In this work, we develop a quantitative methodology using digital image analysis techniques to differentiate impact bloodstain patterns. The bloodstain patterns were digitally imaged and analyzed using image analysis algorithms. Our analysis of 72 unique bloodstain patterns, comprising more than 490,000 individual droplet stains, indicates that the mean drop size in a gunshot spatter pattern is at most 30% smaller than the mean drop stain size in blunt instrument patterns. In contrast, we demonstrate that the spatial distribution of the droplet stains-their density as a function of position in the pattern-significantly differs between gunshot and blunt instrument patterns, with densities as much as 400% larger for gunshot impacts. Thus, quantitative metrics involving the spatial distribution of droplet stains within a bloodstain pattern can be useful for objective differentiation between blunt instrument and gunshot bloodstain patterns.