Sensitivity of snow density and specific surface area measured by microtomography to different image processing algorithms

• Published in: The Cryosphere
• Main Authors: Hagenmuller, Pascal, Matzl, Margret, Chambon, Guillaume, Schneebeli, Martin
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2016
• Subjects: article; Verlagsveröffentlichung; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-10-1039-2016; https://noa.gwlb.de/receive/cop_mods_00013366; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00013322/tc-10-1039-2016.pdf; https://tc.copernicus.org/articles/10/1039/2016/tc-10-1039-2016.pdf

Microtomography can measure the X-ray attenuation coefficient in a 3-D volume of snow with a spatial resolution of a few microns. In order to extract quantitative characteristics of the microstructure, such as the specific surface area (SSA), from these data, the greyscale image first needs to be segmented into a binary image of ice and air. Different numerical algorithms can then be used to compute the surface area of the binary image. In this paper, we report on the effect of commonly used segmentation and surface area computation techniques on the evaluation of density and specific surface area. The evaluation is based on a set of 38 X-ray tomographies of different snow samples without impregnation, scanned with an effective voxel size of 10 and 18 μm. We found that different surface area computation methods can induce relative variations up to 5 % in the density and SSA values. Regarding segmentation, similar results were obtained by sequential and energy-based approaches, provided the associated parameters were correctly chosen. The voxel size also appears to affect the values of density and SSA, but because images with the higher resolution also show the higher noise level, it was not possible to draw a definitive conclusion on this effect of resolution.