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CobWeb 1.0: machine learning toolbox for tomographic imaging

Chauhan, Swarup ; Sell, Kathleen ; Rühaak, Wolfram ; Wille, Thorsten ; Sass, Ingo (2020)
CobWeb 1.0: machine learning toolbox for tomographic imaging.
In: Geoscientific Model Development, 2020, 13 (1)
doi: 10.25534/tuprints-00013340
Article, Secondary publication, Publisher's Version

Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

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Item Type: Article
Type of entry: Secondary publication
Title: CobWeb 1.0: machine learning toolbox for tomographic imaging
Language: English
Date: 2020
Year of primary publication: 2020
Publisher: Copernicus
Journal or Publication Title: Geoscientific Model Development
Volume of the journal: 13
Issue Number: 1
DOI: 10.25534/tuprints-00013340
URL / URN: https://doi.org/10.5194/gmd-13-315-2020
Origin: Secondary publication via sponsored Golden Open Access

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.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-133406
Classification DDC: 500 Science and mathematics > 550 Earth sciences and geology
Divisions: 11 Department of Materials and Earth Sciences > Earth Science > Geothermal Science and Technology
Zentrale Einrichtungen > University and State Library Darmstadt (ULB)
Date Deposited: 21 Aug 2020 08:45
Last Modified: 21 Aug 2020 08:55
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/13340
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