Detection and Discrimination of Bacterial Colonies with Mueller Matrix Imaging

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作者
Saeedesadat Badieyan
Arezou Dilmaghani-Marand
Mohammad Javad Hajipour
Ali Ameri
Mohammad Reza Razzaghi
Hashem Rafii-Tabar
Morteza Mahmoudi
Pezhman Sasanpour
机构
[1] School of Medicine,Department of Medical Physics and Biomedical Engineering
[2] Shahid Beheshti University of Medical Sciences,Non
[3] Endocrinology and Metabolism Population Sciences Institute,Communicable Diseases Research Center
[4] Tehran University of Medical Sciences,Persian Gulf Marine Biotechnology Research Center
[5] The Persian Gulf Biomedical Sciences Research Institute,Department of Urology, Shohada
[6] Bushehr University of Medical Sciences,e
[7] Shahid Beheshti University of Medical Sciences,Tajrish Hospital
[8] Brigham and Women’s Hospital,Department of Anesthesiology
[9] Harvard Medical School,undefined
[10] Boston,undefined
[11] School of Nanoscience,undefined
[12] Institute for Research in Fundamental Sciences (IPM),undefined
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摘要
The polarization imaging technique is a powerful approach to probe microstructural and optical information of biological structures (e.g., tissue samples). Here, we have studied the polarization properties of different bacterial colonies in order to evaluate the possibility of bacterial detection and discrimination. In this regard, we have taken the backscattering Mueller matrix images of four different bacteria colonies (i.e., Escherichia coli, Lactobacillus rhamnosus, Rhodococcus erythropolis, and Staphylococcus aureus). Although the images have the potential to distinguish qualitatively different bacterial colonies, we explored more accurate and quantitative parameters criteria for discrimination of bacterial samples; more specifically, we have exploited the Mueller matrix polar decomposition (MMPD),frequency distribution histogram (FDH), and central moment analysis method. The outcomes demonstrated a superior capacity of Mueller matrix imaging, MMPD, and FDH in bacterial colonies identification and discrimination. This approach might pave the way for a reliable, efficient, and cheap way of identification of infectious diseases.
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