Segmentation of X‐ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures

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Observer-dependent variability of the thresholding step in the quantitative analysis of soil images and X-ray microtomography data (2010, background): “A similar variation in thresholding outputs was observed recently by Iassonov et al. (2009), who applied 14 algorithms to industrial and synchrotron computed tomography images of macroporous soils, sand-bentonitemixtures, and precision glass beads.”
3D numerical reconstruction of well-connected porous structure of rock using fractal algorithms (2014, background): “As pointed out in [35], while local segmentation methods [36,37] can generally provide better segmentation quality and account for some image artifacts (e.”
New Local Thresholding Method for Soil Images by Minimizing Grayscale Intra‐Class Variance (2013, background): “Most of the existing segmentation methods are not designed for porous materials of this complexity and therefore cannot cope adequately, or require significant supervision by a skilled operator to achieve acceptable segmentations (Sezgin and Sankur, 2004; Iassonov et al., 2009; Wang et al., 2011).”
Image processing of multiphase images obtained via X‐ray microtomography: A review (2014, methodology): “A reported drawback of this method is its sensitivity to seed region detection and the adjustment of parameters for the speed function [Iassonov et al., 2009].”