Imaging by Algorithm - Integration of traditional, multispectral, computational, and medical imaging in forensic documentation

The documentation of victims and their associated scenes requires a structured and technically informed approach to accurately capture trauma, disease, identifying features, and trace evidence. Each case presents unique, time-sensitive challenges shaped by lighting conditions, environmental factors, and operational constraints. Under these pressures, investigators often struggle to determine the optimal sequencing of techniques, wavelengths, and 2D/3D modalities. When each option is treated as a discrete decision rather than part of an integrated workflow, the risk of incomplete or inconsistent documentation increases.

This presentation introduces a forensic imaging decision-tree model that unifies traditional photography, multispectral imaging, and 3D capture within a single algorithmic framework. The model provides a guided top-down approach that helps investigators anticipate available options, understand expected outcomes before imaging begins, and incorporate practical considerations—such as lighting selection, camera type, and scale placement—into a defensible and methodical workflow.

The framework outlines appropriate sequencing of multispectral techniques, including polarization, reflected infrared and ultraviolet imaging, induced fluorescence, and advanced off-camera lighting. It also integrates 3D capture and medical imaging workflows, connecting computational photography with X-ray, computed tomography (CT), and magnetic resonance imaging (MRI). Together, these modalities produce detailed, metrically reliable reconstructions that reveal information not visible under white-light imaging alone.