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Peer Reviewed Manuscripts

Thatcher et al. Imaging techniques for clinical burn assessment with a focus on multispectral imaging. Advances in Wound Care 2016; epub ahead of print. (Publication)

Thatcher et al. Multispectral and photoplethysmography optical imaging techniques identify important tissue characteristics in an animal model of tangential burn excision. Journal of Burn Care & Research 2016;37:38-52. (Publication)

Li et al. Outlier detection and removal improves accuracy of machine learning approach to multispectral burn diagnostic imaging. Journal of Biomedical Optics 2015;20:121305. (Publication)

King et al. Surgical wound debridement sequentially characterized in a porcine burn model with multispectral imaging. Burns 2015;41:1478-1487. (Publication)

Moza et al. Deep-tissue dynamic monitoring of decubitus ulcers: wound care and assessment. IEEE Engineering in Medicine and Biology Magazine 2010;29:71-77. (Publication)


Conference Proceedings

Squiers et al. Multispectral imaging burn wound tissue classification system: a comparison of test accuracies of several common machine learning algorithms. SPIE Proceedings Vol. 9785, Medical imaging 2016: Computer-Aided Diagnosis 2016:97853L. (Publication)

Li et al. Burn injury diagnostic imaging device's accuracy improved by outlier detection and removal. SPIE Proceedings Vol. 9472, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI 2015:947206. (Publication)

Mo et al. The importance of illumination in a non-contact photoplethysmography imaging system for burn wound assessment. SPIE Proceedings Vol. 9303, Photonic Therapeutics and Diagnostics XI 2015:9303M. (Publication)

Thatcher et al. Dynamic tissue phantoms and their use in assessment of a noninvasive optical plethysmography imaging device. SPIE Proceedings Vol. 9107, Smart Biomedical and Physiological Sensor Technology XI 2014:910718. (Publication)