Chauhan, Swarup ; Sell, Kathleen ; Rühaak, Wolfram ; Wille, Thorsten ; Sass, Ingo (2020)
CobWeb 1.0: machine learning toolbox for tomographic imaging.
In: Geoscientific Model Development, 13 (1)
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (Abstract)
Despite the availability of both commercial and open-source software, an ideal tool for digital rock physics analysis for accurate automatic image analysis at ambient computational performance is difficult to pinpoint. More often, image segmentation is driven manually, where the performance remains limited to two phases. Discrepancies due to artefacts cause inaccuracies in image analysis. To overcome these problems, we have developed CobWeb 1.0, which is automated and explicitly tailored for accurate greyscale (multiphase) image segmentation using unsupervised and supervised machine learning techniques. In this study, we demonstrate image segmentation using unsupervised machine learning techniques. The simple and intuitive layout of the graphical user interface enables easy access to perform image enhancement and image segmentation, and further to obtain the accuracy of different segmented classes. The graphical user interface enables not only processing of a full 3-D digital rock dataset but also provides a quick and easy region-of-interest selection, where a representative elementary volume can be extracted and processed. The CobWeb software package covers image processing and machine learning libraries of MATLAB® used for image enhancement and image segmentation operations, which are compiled into series of Windows-executable binaries. Segmentation can be performed using unsupervised, supervised and ensemble classification tools. Additionally, based on the segmented phases, geometrical parameters such as pore size distribution, relative porosity trends and volume fraction can be calculated and visualized. The CobWeb software allows the export of data to various formats such as ParaView (.vtk), DSI Studio (.fib) for visualization and animation, and Microsoft® Excel and MATLAB® for numerical calculation and simulations. The capability of this new software is verified using high-resolution synchrotron tomography datasets, as well as lab-based (cone-beam) X-ray microtomography datasets. Regardless of the high spatial resolution (submicrometre), the synchrotron dataset contained edge enhancement artefacts which were eliminated using a novel dual filtering and dual segmentation procedure.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Chauhan, Swarup ; Sell, Kathleen ; Rühaak, Wolfram ; Wille, Thorsten ; Sass, Ingo |
| Art des Eintrags: | Bibliographie |
| Titel: | CobWeb 1.0: machine learning toolbox for tomographic imaging |
| Sprache: | Englisch |
| Publikationsjahr: | 2020 |
| Verlag: | Copernicus |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Geoscientific Model Development |
| Jahrgang/Volume einer Zeitschrift: | 13 |
| (Heft-)Nummer: | 1 |
| URL / URN: | https://doi.org/10.5194/gmd-13-315-2020 |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Despite the availability of both commercial and open-source software, an ideal tool for digital rock physics analysis for accurate automatic image analysis at ambient computational performance is difficult to pinpoint. More often, image segmentation is driven manually, where the performance remains limited to two phases. Discrepancies due to artefacts cause inaccuracies in image analysis. To overcome these problems, we have developed CobWeb 1.0, which is automated and explicitly tailored for accurate greyscale (multiphase) image segmentation using unsupervised and supervised machine learning techniques. In this study, we demonstrate image segmentation using unsupervised machine learning techniques. The simple and intuitive layout of the graphical user interface enables easy access to perform image enhancement and image segmentation, and further to obtain the accuracy of different segmented classes. The graphical user interface enables not only processing of a full 3-D digital rock dataset but also provides a quick and easy region-of-interest selection, where a representative elementary volume can be extracted and processed. The CobWeb software package covers image processing and machine learning libraries of MATLAB® used for image enhancement and image segmentation operations, which are compiled into series of Windows-executable binaries. Segmentation can be performed using unsupervised, supervised and ensemble classification tools. Additionally, based on the segmented phases, geometrical parameters such as pore size distribution, relative porosity trends and volume fraction can be calculated and visualized. The CobWeb software allows the export of data to various formats such as ParaView (.vtk), DSI Studio (.fib) for visualization and animation, and Microsoft® Excel and MATLAB® for numerical calculation and simulations. The capability of this new software is verified using high-resolution synchrotron tomography datasets, as well as lab-based (cone-beam) X-ray microtomography datasets. Regardless of the high spatial resolution (submicrometre), the synchrotron dataset contained edge enhancement artefacts which were eliminated using a novel dual filtering and dual segmentation procedure. |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 550 Geowissenschaften |
| Fachbereich(e)/-gebiet(e): | 11 Fachbereich Material- und Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Geowissenschaften 11 Fachbereich Material- und Geowissenschaften > Geowissenschaften > Fachgebiet Angewandte Geothermie Zentrale Einrichtungen Zentrale Einrichtungen > Universitäts- und Landesbibliothek (ULB) |
| Hinterlegungsdatum: | 02 Aug 2024 12:34 |
| Letzte Änderung: | 02 Aug 2024 12:34 |
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CobWeb 1.0: machine learning toolbox for tomographic imaging. (deposited 21 Aug 2020 08:45)
- CobWeb 1.0: machine learning toolbox for tomographic imaging. (deposited 02 Aug 2024 12:34) [Gegenwärtig angezeigt]
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