2D Imaging Simulation from 3D Particle Data: Python Notebooks, Particle Datasets, and Simulation Results
Contributing person | Ralf Ditscherlein | |
Contributing person | Urs A. Peuker | |
Countries to which the data refer | GERMANY | |
Description of the data | Original particle 3D data comes in the form of STL files for three types of solids: aluminium oxide, soda-lime glass, dolomite, mica, limestone, and quartz. STL files should all represent closed meshes and have been generated from VTK data derived from reconstructed tomography data. Please refer to the publication of Ditscherlein et. al (2022) for the desription of the measurement and origin of the solids: Ditscherlein R, Furat O, Löwer E, et al. PARROT: A Pilot Study on the Open Access Provision of Particle-Discrete Tomographic Datasets. Microscopy and Microanalysis. 2022;28(2):350-360. doi:10.1017/S143192762101391X | |
Type of the data | Dataset | |
Type of the data | Model | |
Total size of the dataset | 2386796512 | |
Author | Buchwald, Thomas | |
Author | Ditscherlein, Ralf | |
Upload date | 2024-05-14T13:21:54Z | |
Publication date | 2024-05-14T13:21:54Z | |
Data of data creation | 2023-12-14 | |
Publication date | 2024-05-14 | |
Abstract of the dataset | 2D projection imaging techniques are simulated with 3D particle data from tomography measurements. This submission contains three distinct parts: original particle data, Python notebooks to simulate 2D imaging of the particles, and the resulting simulation dataset. The particle data comes as STLs that have been converted from VTK particle data as provided by the PARROT database (https://parrot.tu-freiberg.de/). The STL files are *not* identical with the STLs provided by PARROT as of May 2024! The particle data folder is provided as separate archive because of its large size. The Python notebooks were created with Jupyter Lab and Anaconda. A environment.yml file is provided that recreates the Anaconda environment. The simulation dataset that results from the provided Python notebooks is made available as CSV files or as pickled Python (pandas) DataFrames. Please refer to the included Readme for a detailed description of the files contained in the archive. | |
Public reference to this page | https://opara.zih.tu-dresden.de//handle/123456789/628 | |
Public reference to this page | https://doi.org/10.25532/OPARA-479 | |
Publisher | Technische Universität Bergakademie Freiberg | |
Licence | Attribution 4.0 International | en |
URI of the licence text | http://creativecommons.org/licenses/by/4.0/ | |
Specification of the discipline(s) | 4::42::403::403-03 | |
Specification of the discipline(s) | 4::43::406::406-04 | |
Title of the dataset | 2D Imaging Simulation from 3D Particle Data: Python Notebooks, Particle Datasets, and Simulation Results | |
Underlying research object | solid particles of several origins | |
Software | Python 3.11 | |
Project abstract | The 3D size and shape of particles is usually estimated from 2D imaging techniques. The correlation of 2D and 3D shape factors has been the subject of several publications over the past decades. Because most research is necessarily limited to the simulation of 2D imaging of simple mathematical shapes, the results are limited with regards to the applicability to real-life measurement scenarios. Here, a particle dataset of 3D particle shapes of six solids, as measured in an X-ray microsope, are used to simulate 2D imaging techniques. Static image analysis is simulated by aligning the particles to their principal axes of inertia or by orienting them into stable positions and producing projection silhouettes afterwards. Dynamic image analysis is simulated by randomly orienting the particles in space and producing projection silhouettes from that. Equivalent diameters and shape factors are calculated for 3D and 2D geometric measures for the purpose of correlation. | |
Project title | 2D Particle Imaging Simulation |
Files
Original bundle
- Name:
- image_analysis_simulation.zip
- Size:
- 41.47 MB
- Format:
- Unknown data format
- Description:
- This archive contains notebooks and resulting simulation tables, but not the underlying particle data. Download this archive only if you do not need to simulate static or dynamic image analysis, but only need the results.
- Name:
- particle_data.zip
- Size:
- 2.18 GB
- Format:
- Unknown data format
- Description:
- Particle data that needs to be extracted into the root directory of the main data file if the dataset needs to be reproduced. Note that the uncompressed files are well above 30 GB!
License bundle
- Name:
- license.txt
- Size:
- 4.66 KB
- Format:
- Item-specific license agreed to upon submission
- Description:
Collections
