Statistical segmentation and porosity quantification of 3D x-ray microtomography

Ushizima, Daniela; Parkinson, Dilworth; Nico, Peter; Ajo‐Franklin, Jonathan; MacDowell, Alastair A.; Kocar, Benjamin D.; Bethel, Wes; Sethian, James A.

doi:10.1117/12.892809

Cited by 14 publications

(15 citation statements)

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“…A more recent capability CAMERA is developing is for fiber and crack analysis, which has been applied to ceramic matrix composites. 6,7 FIGURE 3. Screenshot of 3D web viewer; volume is rendered on a high performance computer, and the resulting image is passed to the web client.…”

Section: Example 1: Microtomography Data Management and Image Processingmentioning

confidence: 99%

Real-time data-intensive computing

Parkinson

Beattie

Chen

et al. 2016

AIP Conference Proceedings

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Section: Example 1: Microtomography Data Management and Image Processingmentioning

confidence: 99%

Real-time data-intensive computing

Parkinson

Beattie

Chen

et al. 2016

AIP Conference Proceedings

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“…Ushizima et al [21,22] describe a computational analysis workflow for carbon sequestration research that includes image filtering, segmentation, and feature detection and analysis. This pipeline includes algorithms that analyze microCT data sets from soil samples, rocks, glass beads and simulated data, representing jammed packing of glass beads.…”

Section: Image Analysismentioning

confidence: 99%

“…This pipeline includes algorithms that analyze microCT data sets from soil samples, rocks, glass beads and simulated data, representing jammed packing of glass beads. They introduced and designed a new algorithm and software framework, Quant-CT [21,22], which processes and quantifies structures from cross-sections, and uses VisIt [6] to visualize results as illustrated in Fig. 1 and Fig.…”

Section: Image Analysismentioning

confidence: 99%

“…The current algorithms can differentiate porous media (high density material) from void (background, low density media) using a Boolean classifier, and extract features such as volume, surface area, granularity spectrum, porosity, and more recently, permeability. Quant-CT supports quick user interaction, including the ability for the user to train the algorithm via subsamples, and provide its core algorithms with an automated parametrization [21]. As illustrated in Fig.…”

Section: Image Analysismentioning

confidence: 99%

“…One of the challenges of using BF is finding good parametrizations of the photometric and the geometric kernels for anisotropic smoothing. Ushizima et al showed that one could calculate the photometric parameter as proportional to the signal-to-noise ratio [21], aiming to minimize the issue of reflection over the large spectrum of X-ray attenuation values of the scanned materials. This enables automated tuning of scale parameters, based on statistics extracted from patches fed into the filtering algorithm by the user.…”

Section: Image Analysismentioning

confidence: 99%

See 2 more Smart Citations

Augmented Topological Descriptors of Pore Networks for Material Science

Ushizima

Morozov

Weber

et al. 2012

IEEE Trans. Visual. Comput. Graphics

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Abstract-One potential solution to reduce the concentration of carbon dioxide in the atmosphere is the geologic storage of captured CO 2 in underground rock formations, also known as carbon sequestration. There is ongoing research to guarantee that this process is both efficient and safe. We describe tools that provide measurements of media porosity, and permeability estimates, including visualization of pore structures. Existing standard algorithms make limited use of geometric information in calculating permeability of complex microstructures. This quantity is important for the analysis of biomineralization, a subsurface process that can affect physical properties of porous media. This paper introduces geometric and topological descriptors that enhance the estimation of material permeability. Our analysis framework includes the processing of experimental data, segmentation, and feature extraction and making novel use of multiscale topological analysis to quantify maximum flow through porous networks. We illustrate our results using synchrotron-based X-ray computed microtomography of glass beads during biomineralization. We also benchmark the proposed algorithms using simulated data sets modeling jammed packed bead beds of a monodispersive material.

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