An average enumeration method of hyperspectral imaging data for quantitative evaluation of medical device surface contamination

Hanh N D Le; Moon S Kim; Jeeseong Hwang; Yi Yang; Paweena U Thainual; Jin U Kang; Do-Hyun Kim

doi:10.1364/BOE.5.003613

An average enumeration method of hyperspectral imaging data for quantitative evaluation of medical device surface contamination

Biomed Opt Express. 2014 Sep 15;5(10):3613-27. doi: 10.1364/BOE.5.003613. eCollection 2014 Oct 1.

Authors

Hanh N D Le¹, Moon S Kim², Jeeseong Hwang³, Yi Yang⁴, Paweena U Thainual⁴, Jin U Kang⁵, Do-Hyun Kim⁴

Affiliations

¹ Food 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.
² United States Department of Agriculture, 1400 Independence Ave., S.W, Washington, D.C. 20250, USA.
³ National Institute of Standards and Technology, 325 Broadway St., Boulder, C.O. 80305, USA.
⁴ Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, M.D. 20993, USA.
⁵ 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.

Keywords: (110.4234) Multispectral and hyperspectral imaging; (170.3880) Medical and biological imaging.