Techniques for image enhancement and segmentation of tomographic images of porous materials

By Adrian P. Sheppard, R. Sok, H. Averdunk. 2004

420 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Mechanics of Materials, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1e4ef25cc0f3bc78f4b7e3bbd306a3607cf770e4

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In Computer Vision and Pattern Recognition

In Mechanics of Materials

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Image processing of multiphase images obtained via X‐ray microtomography: A review (2014, methodology): “…we will examine hysteresis segmentation [Vogel and Kretzschmar, 1996], indicator kriging [Oh and Lindquist, 1999], converging active contours [Sheppard et al., 2004], watershed segmentation [Vincent and Soille, 1991], and Bayesian Markov random field segmentation [Kulkarni et al., 2012].”
Pore-scale contact angle measurements at reservoir conditions using X-ray microtomography (2014, methodology): “As segmentation of images containing a partial saturation of multiple fluids is significantly more difficult than the segmentation of dry images [32], the use of simple grey-scale segmentation was insufficient.”
Investigating the geometrical structure of disordered sphere packings (2004, methodology): “After segmentation (see Sheppard et al in this issue [8]) the sample data sets are reduced to threedimensional binary images, representing two distinct phases, one associated with the spheres and the other with air space.”
Image analysis algorithms for estimating porous media multiphase flow variables from computed microtomography data: a validation study (2010, background): “, [32, 34, 35, 38, 48]), and Sezgin and Sankur [47] provide a comprehensive survey of 40 segmentation methods.”
Pore‐by‐pore capillary pressure measurements using X‐ray microtomography at reservoir conditions: Curvature, snap‐off, and remobilization of residual CO2 (2014, methodology): “The segmentation of images containing a partial saturation of multiple fluids is significantly more complicated than the segmentation of dry images, so the use of simple gray scale universal thresholding was insufficient [Sheppard et al., 2004].”
Pore-Scale Characterization of Two-Phase Flow Using Integral Geometry (2017, methodology): “The images were segmented using gradient-based imaged segmentation techniques (Schlüter et al. 2014; Sheppard et al. 2004) and the identified WP andNWPwere characterized using image quantification software that has been presented elsewhere (Arns et al.”
Segmentation of X‐ray computed tomography images of porous materials: A crucial step for characterization and quantitative analysis of pore structures (2009, methodology): “…considered as a refinement of global thresholding based on local spatial information such as a two-point local covariance function of the image [Oh and Lindquist, 1999; Mardia and Hainsworth, 1988], or edge detection [Yanowitz and Bruckstein, 1989; Hertz and Schafer, 1988; Sheppard et al., 2004].”
Imaging and image processing in porous media research (2008, background): “[37] combined diffusion filters and unsharp mask [24] edge enhancement for tomographic images of porous materials.”