Open AccessAn average enumeration method of hyperspectral imaging data for quantitative evaluation of medical device surface contamination
Author(s) -
Hanh N. D. Le,
Moon S. Kim,
Jeeseong Hwang,
Yi Yang,
Paweena U Thainual,
Jin U. Kang,
Dohyun Kim
Publication year - 2014
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.5.003613
Subject(s) - hyperspectral imaging , principal component analysis , contamination , enumeration , computer science , sample (material) , spectral imaging , artificial intelligence , remote sensing , pattern recognition (psychology) , computer vision , mathematics , chromatography , chemistry , geology , ecology , combinatorics , biology
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.