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AuthorMalik J.
AuthorKiranyaz, Mustafa Serkan
AuthorAl-Raoush R.I.
AuthorMonga O.
AuthorGarnier P.
AuthorFoufou S.
AuthorBouras A.
AuthorIosifidis A.
AuthorGabbouj M.
AuthorBaveye P.C.
Available date2022-04-26T12:31:16Z
Publication Date2022
Publication NameComputers and Geosciences
ResourceScopus
Identifierhttp://dx.doi.org/10.1016/j.cageo.2021.105017
URIhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85120657547&doi=10.1016%2fj.cageo.2021.105017&partnerID=40&md5=137e2e4460303b95119a37d4a4ab9330
URIhttp://hdl.handle.net/10576/30577
AbstractBinary segmentation of volumetric images of porous media is a crucial step towards gaining a deeper understanding of the factors governing biogeochemical processes at minute scales. Contemporary work primarily revolves around primitive techniques based on global or local adaptive thresholding that have known common drawbacks in image segmentation. Moreover, the absence of a unified benchmark prohibits quantitative evaluation, which further undermines the impact of existing methodologies. In this study, we tackle the issue on both fronts. First, by drawing parallels with natural image segmentation, we propose a novel, and automatic segmentation technique, 3D Quantum Cuts (QCuts-3D) grounded on a state-of-the-art spectral clustering technique. Secondly, we curate and present a publicly available dataset of 68 multiphase volumetric images of porous media with diverse solid geometries, along with voxel-wise ground truth annotations for each constituting phase. We provide comparative evaluations between QCuts-3D and the current state-of-the-art over this dataset across a variety of evaluation metrics. The proposed systematic approach achieves a 26% increase in AUROC (Area Under Receiver Operating Characteristics) while achieving a substantial reduction of the computational complexity over state-of-the-art competitors. Moreover, statistical analysis reveals that the proposed method exhibits significant robustness against the compositional variations of porous media.
Languageen
PublisherElsevier Ltd
SubjectClustering algorithms
Graphic methods
Image segmentation
Quantum theory
Tomography
Automatic segmentations
Binary segmentation
Biogeochemical process
Graph-cut
Images segmentations
Local adaptive thresholding
Porous medium
Soil segmentation
State of the art
Volumetric images
Porous materials
comparative study
image analysis
porous medium
three-dimensional modeling
Title3D Quantum Cuts for automatic segmentation of porous media in tomography images
TypeArticle
Volume Number159
dc.accessType Abstract Only


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