Three-dimensional multiphase segmentation of X-ray CT data of porous materials using a Bayesian Markov random field framework

Ramaprasad Kulkarni, Markus Tuller, Wolfgang Fink, Dorthe Wildenschild

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Advancements in noninvasive imaging methods such as X-ray computed tomography (CT) have led to a recent surge of applications in porous media research with objectives ranging from theoretical aspects of pore-scale fluid and interfacial dynamics to practical applications such as enhanced oil recovery and advanced contaminant remediation. While substantial efforts and resources have been devoted to advance CT technology, microscale analysis, and fluid dynamics simulations, the development of efficient and stable threedimensional multiphase image segmentation methods applicable to large data sets is lacking. To eliminate the need for wet-dry or dual-energy scans, image alignment, and subtraction analysis, commonly applied in X-ray micro-CT, a segmentation method based on a Bayesian Markov random field (MRF) framework amenable to true three-dimensional multiphase processing was developed and evaluated. Furthermore, several heuristic and deterministic combinatorial optimization schemes required to solve the labeling problem of the MRF image model were implemented and tested for computational efficiency and their impact on segmentation results. Test results for three grayscale data sets consisting of dry glass beads, partially saturated glass beads, and partially saturated crushed tuffobtained with synchrotron X-ray micro-CT demonstrate great potential of the MRF image model for three-dimensional multiphase segmentation. While our results are promising and the developed algorithm is stable and computationally more efficient than other commonly applied porous media segmentation models, further potential improvements exist for fully automated operation.

Original languageEnglish (US)
JournalVadose Zone Journal
Volume11
Issue number1
DOIs
StatePublished - Feb 2012

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computed tomography
segmentation
tomography
micro-computed tomography
X-radiation
porous media
glass
porous medium
remediation
fluid mechanics
imaging method
methodology
image analysis
enhanced oil recovery
fluid dynamics
oils
heuristics
energy
material
testing

ASJC Scopus subject areas

  • Soil Science

Cite this

Three-dimensional multiphase segmentation of X-ray CT data of porous materials using a Bayesian Markov random field framework. / Kulkarni, Ramaprasad; Tuller, Markus; Fink, Wolfgang; Wildenschild, Dorthe.

In: Vadose Zone Journal, Vol. 11, No. 1, 02.2012.

Research output: Contribution to journalArticle

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