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Micro-computed tomography of sandstone rocks: Raw, filtered and segmented datasets
High-resolution computed micro-tomography is an important area of science, which correlates well with several experimental methodologies and serves as a basis for advanced computational physics studies, in which high-resolution images are used as input to different scientific simulation models. The...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844843/ https://www.ncbi.nlm.nih.gov/pubmed/35198674 http://dx.doi.org/10.1016/j.dib.2022.107893 |
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author | Lucas-Oliveira, Everton Barsi-Andreeta, Mariane Neumann, Rodrigo F. Trevizan, Willian A. Steiner, Mathias B. Bonagamba, Tito J. |
author_facet | Lucas-Oliveira, Everton Barsi-Andreeta, Mariane Neumann, Rodrigo F. Trevizan, Willian A. Steiner, Mathias B. Bonagamba, Tito J. |
author_sort | Lucas-Oliveira, Everton |
collection | PubMed |
description | High-resolution computed micro-tomography is an important area of science, which correlates well with several experimental methodologies and serves as a basis for advanced computational physics studies, in which high-resolution images are used as input to different scientific simulation models. The dataset presented herein includes (raw) grayscale images obtained using the Bruker Skyscan 1272 X-Ray tomograph; filtered images acquired through contrast enhancement and noise reduction filters; and segmented images obtained by using the IsoData segmentation method. All images have a resolution of 2.25 µm (isometric voxels) and size of 1000(3) voxels. |
format | Online Article Text |
id | pubmed-8844843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-88448432022-02-22 Micro-computed tomography of sandstone rocks: Raw, filtered and segmented datasets Lucas-Oliveira, Everton Barsi-Andreeta, Mariane Neumann, Rodrigo F. Trevizan, Willian A. Steiner, Mathias B. Bonagamba, Tito J. Data Brief Data Article High-resolution computed micro-tomography is an important area of science, which correlates well with several experimental methodologies and serves as a basis for advanced computational physics studies, in which high-resolution images are used as input to different scientific simulation models. The dataset presented herein includes (raw) grayscale images obtained using the Bruker Skyscan 1272 X-Ray tomograph; filtered images acquired through contrast enhancement and noise reduction filters; and segmented images obtained by using the IsoData segmentation method. All images have a resolution of 2.25 µm (isometric voxels) and size of 1000(3) voxels. Elsevier 2022-02-02 /pmc/articles/PMC8844843/ /pubmed/35198674 http://dx.doi.org/10.1016/j.dib.2022.107893 Text en © 2022 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Lucas-Oliveira, Everton Barsi-Andreeta, Mariane Neumann, Rodrigo F. Trevizan, Willian A. Steiner, Mathias B. Bonagamba, Tito J. Micro-computed tomography of sandstone rocks: Raw, filtered and segmented datasets |
title | Micro-computed tomography of sandstone rocks: Raw, filtered and segmented datasets |
title_full | Micro-computed tomography of sandstone rocks: Raw, filtered and segmented datasets |
title_fullStr | Micro-computed tomography of sandstone rocks: Raw, filtered and segmented datasets |
title_full_unstemmed | Micro-computed tomography of sandstone rocks: Raw, filtered and segmented datasets |
title_short | Micro-computed tomography of sandstone rocks: Raw, filtered and segmented datasets |
title_sort | micro-computed tomography of sandstone rocks: raw, filtered and segmented datasets |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8844843/ https://www.ncbi.nlm.nih.gov/pubmed/35198674 http://dx.doi.org/10.1016/j.dib.2022.107893 |
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