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Precision of light intensity measurement in biological optical microscopy
Author(s) -
BERNAS TYTUS,
BARNES DAVID,
ASEM ELIKPLIMI K.,
ROBINSON J. PAUL,
RAJWA BARTEK
Publication year - 2007
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/j.1365-2818.2007.01764.x
Subject(s) - noise (video) , microscope , calibration , microscopy , computer science , sensitivity (control systems) , optics , dynamic range , multiplicative noise , signal (programming language) , fixed pattern noise , pixel , artificial intelligence , computer vision , transmission (telecommunications) , analog signal , physics , mathematics , electronic engineering , signal transfer function , statistics , telecommunications , image (mathematics) , programming language , engineering
Summary Standardization and calibration of optical microscopy systems have become an important issue owing to the increasing role of biological imaging in high‐content screening technology. The proper interpretation of data from high‐content screening imaging experiments requires detailed information about the capabilities of the systems, including their available dynamic range, sensitivity and noise. Currently available techniques for calibration and standardization of digital microscopes commonly used in cell biology laboratories provide an estimation of stability and measurement precision (noise) of an imaging system at a single level of signal intensity. In addition, only the total noise level, not its characteristics (spectrum), is measured. We propose a novel technique for estimation of temporal variability of signal and noise in microscopic imaging. The method requires registration of a time series of images of any stationary biological specimen. The subsequent analysis involves a multi‐step process, which separates monotonic, periodic and random components of every pixel intensity change in time. The technique allows simultaneous determination of dark, photonic and multiplicative components of noise present in biological measurements. Consequently, a respective confidence interval (noise level) is obtained for each level of signal. The technique is validated using test sets of biological images with known signal and noise characteristics. The method is also applied to assess uncertainty of measurement obtained with two CCD cameras in a wide‐field microscope.