Tunable filter-based multispectral imaging for detection of blood stains on construction material substrates Part 1 Developing blood stain discrimination criteria

Abstract

In this article, we establish blood stain detection criteria that are less substrate dependent for use in a liquid crystal tunable filter-based multispectral-imaging system. Kubelka-Munk (KM) theory is applied to transform the acquired stains' reflectance spectra into the less substrate dependent spectra. Chosen spectral parameters are extracted from the KM absorbance spectra of several stain samples on several substrates. Blood discrimination criteria based upon those spectral parameters are then established from empirical data, tested, and refined. In our newly invented method, instead of introducing conventional contrast enhancement on the blood stain image, blood stain determination is executed mathematically via Boolean logic, resulting in more discriminative blood stain identification. This proposed approach allows for nondestructive, quick, discriminative, and easy-to-improve presumptive blood stain detection. Experimental results confirm that our blood stain discrimination criteria can be used to locate blood stains on several construction materials with high precision. True positive rates (sensitivity) from 0.60 to 0.95 are achieved depending on blood stain faintness and substrate types. Also, true negative rates (specificity) between 0.55 and 0.96 and identification time of 4-5 min are accomplished, respectively. The established blood stain discrimination criteria will be incorporated in a real blood stain detection system in part 2 of this article, where system design and considerations as well as speed issues are discussed.

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