Paper
30 March 2007 Multispectral image analysis of bruise age
Author Affiliations +
Abstract
The detection and aging of bruises is important within clinical and forensic environments. Traditionally, visual and photographic assessment of bruise color is used to determine age, but this substantially subjective technique has been shown to be inaccurate and unreliable. The purpose of this study was to develop a technique to spectrally-age bruises using a reflective multi-spectral imaging system that minimizes the filtering and hardware requirements while achieving acceptable accuracy. This approach will then be incorporated into a handheld, point-of-care technology that is clinically-viable and affordable. Sixteen bruises from elder residents of a long term care facility were imaged over time. A multi-spectral system collected images through eleven narrow band (~10 nm FWHM) filters having center wavelengths ranging between 370-970 nm corresponding to specific skin and blood chromophores. Normalized bruise reflectance (NBR)- defined as the ratio of optical reflectance coefficient of bruised skin over that of normal skin- was calculated for all bruises at all wavelengths. The smallest mean NBR, regardless of bruise age, was found at wavelength between 555 & 577nm suggesting that contrast in bruises are from the hemoglobin, and that they linger for a long duration. A contrast metric, based on the NBR at 460nm and 650nm, was found to be sensitive to age and requires further investigation. Overall, the study identified four key wavelengths that have promise to characterize bruise age. However, the high variability across the bruises imaged in this study complicates the development of a handheld detection system until additional data is available.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stephen Sprigle, Dingrong Yi, Jayme Caspall, Maureen Linden, Linghua Kong, and Mark Duckworth "Multispectral image analysis of bruise age", Proc. SPIE 6514, Medical Imaging 2007: Computer-Aided Diagnosis, 65142T (30 March 2007); https://doi.org/10.1117/12.709930
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Cited by 6 scholarly publications.
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KEYWORDS
Skin

Absorption

Reflectivity

Image filtering

Optical filters

Chromophores

CCD cameras

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