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Variable Threshold Method for Determining the Boundaries of Imaged Subvisible Particles
Published
Author(s)
Richard E. Cavicchi, Cayla Collett, Srivalli Telikepalli, Zhishang Hu, Michael J. Carrier, Dean C. Ripple
Abstract
An accurate assessment of particle characteristics and concentrations in pharmaceutical products by flow imaging requires accurate particle sizing and morphological analysis. Analysis of images begins with the definition of particle boundaries from the captured images. Commonly a single threshold is used to define the level at which a pixel in the image is to be included in the detection of particles. Complicating the analysis is the presence of halos and gradients in intensity near the particle boundaries. There is also a wide range of particle sizes and translucency depending on the particle properties. A single low threshold, designed to detect nearly transparent particles, will result in the oversizing of less transparent particles with more intense halos. Variations in degree of focus further contribute to inaccuracies. We have developed an imaging analysis algorithm that sets the threshold for a particle based on the maximum gray value of the particle. We show that this results in tighter boundaries for particles with high contrast, while conserving the number of highly translucent particles detected. The method is implemented as a plugin for FIJI, an open-source image analysis software. The method is tested for calibration beads in water and glycerol/water solutions, a suspension of microfabricated rods, and stir-stressed aggregates made from IgG. The result is that appropriate thresholds are automatically set for solutions with a range of particle properties, and that improved boundaries will allow for more accurate sizing results and potentially improved particle classification studies.
Cavicchi, R.
, Collett, C.
, Telikepalli, S.
, Hu, Z.
, Carrier, M.
and Ripple, D.
(2017),
Variable Threshold Method for Determining the Boundaries of Imaged Subvisible Particles, Journal of Pharmaceutical Sciences, [online], https://doi.org/10.1016/j.xphs.2017.02.005
(Accessed October 10, 2025)