title: A Quantitative Measure of Field Illumination.
abstract: In this paper, we describe a statistically based algorithm to quantify the uniformity of illumination in an optical light microscopy imaging system that outputs a single quality factor (QF) score. The importance of homogeneous field illumination in quantitative light microscopy is well understood and often checked. However, there is currently no standard automatic quantitative measure of the uniformity of the field illumination. Images from 89 different laser-scanning confocal microscopes (LSCMs), which were collected as part of an international study on microscope quality assessment, were used as a "training" set to build the algorithm. To validate the algorithm and verify its robustness, images from 33 additional microscopes, including LSCM and wide-field (WF) microscopes, were used. The statistical paradigm used for developing the quality scoring scale was a regression approach to supervised learning. Three intensity profiles across each image-2 corner-to-corner diagonals and a center horizontal-were used to generate pixel-intensity data. All of the lines passed through the center of the image. The intensity profile data then were converted into a single-field illumination QF score in the range of 0-100, with 0 having extreme variation, and therefore, essentially unusable, and 100 having no deviation, i.e., straight lines with a constant uniform intensity. Empirically, a QF ≥ 83 was determined to be the minimum acceptable value based on manufacturer acceptance tests and reasonably achievable values. This new QF is an invaluable metric to ascertain objectively and easily the uniformity of illumination quality, provide a traceable reference for monitoring field uniformity over time, and make a direct comparison among different microscopes. The QF can also be used as an indicator of system failure and the need for alignment or service of the instrument.
authors: 1 McGill University, Advanced BioImaging Facility (ABIF), Montreal, Quebec, Canada; 2 Wadsworth Center, New York State Department of Health, Albany, New York, USA; and 3 Department of Biomedical Sciences, School of Public Health, State University of New York at Albany, New York, USA.
authors: Claire M
authors: CM
authors: Brown
authors: 1 McGill University, Advanced BioImaging Facility (ABIF), Montreal, Quebec, Canada; 2 Wadsworth Center, New York State Department of Health, Albany, New York, USA; and 3 Department of Biomedical Sciences, School of Public Health, State University of New York at Albany, New York, USA.
authors: Andrew
authors: A
authors: Reilly
authors: 1 McGill University, Advanced BioImaging Facility (ABIF), Montreal, Quebec, Canada; 2 Wadsworth Center, New York State Department of Health, Albany, New York, USA; and 3 Department of Biomedical Sciences, School of Public Health, State University of New York at Albany, New York, USA.
authors: Richard W
authors: RW
authors: Cole
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008029:Lighting; D018613:Microscopy, Confocal / Q000295:instrumentation / Q000379:methods*; D061848:Optical Imaging / Q000379:methods*
keywords: alignment; flat field; imaging; quality assessment; supervised learning