Extruded styrenic foams are low density foams that are widely used for thermal insulation. It is difficult to precisely
characterize the structure of the cells in low density foams by traditional cross-section viewing due to the frailty of the
walls of the cells. X-ray computed tomography (CT) is a non-destructive, three dimensional structure characterization
technique that has great potential for structure characterization of styrenic foams. Unfortunately the intrinsic artifacts of
the data and the artifacts generated during image reconstruction are often comparable in size and shape to the thin walls
of the foam, making robust and reliable analysis of cell sizes challenging. We explored three different image processing
methods to clean up artifacts in the reconstructed images, thus allowing quantitative three dimensional determination of
cell size in a low density styrenic foam. Three image processing approaches - an intensity based approach, an intensity
variance based approach, and a machine learning based approach - are explored in this study, and the machine learning
image feature classification method was shown to be the best. Individual cells are segmented within the images after the
images were cleaned up using the three different methods and the cell sizes are measured and compared in the study.
Although the collected data with the image analysis methods together did not yield enough measurements for a good
statistic of the measurement of cell sizes, the problem can be resolved by measuring multiple samples or increasing
imaging field of view.
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