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An average enumeration method of hyperspectral imaging data for quantitative evaluation of medical device surface
Hanh N D Le1, Moon S Kim2, Jeeseong Hwang3
1Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, M.D. 20993, USA ; Department of Electrical and Computer Engineering, Johns Hopkins University, 3400 North Charles Street, Baltimore, M.D. 21218, USA.
Abstract:
We propose a quantification method called Mapped Average Principal component analysis Score (MAPS) to enumerate the contamination coverage on common medical device surfaces. The method was adapted from conventional Principal Component Analysis (PCA) on non-overlapped regions of a full frame hyperspectral image to resolve the percentage of contamination from the substrate. The concept was proven by using a controlled contamination sample with artificial test soil and color simulating organic mixture, and was further validated using a bacterial system including biofilm on stainless steel surface. We also validate the results of MAPS with other statistical spectral analysis including Spectral Angle Mapper (SAM). The proposed method provides an alternative quantification method for hyperspectral imaging data, which can be easily implemented by basic PCA analysis.
