This is a submission for the Kuopio Tomography Challenge.
- Amal Mohammed A Alghamdi (DTU), Denmark
- Martin Sæbye Carøe (DTU), Denmark
- Jasper Marijn Everink (DTU), Denmark
- Jakob Sauer Jørgensen (DTU), Denmark
- Kim Knudsen (DTU), Denmark
- Jakob Tore Kammeyer Nielsen (DTU), Denmark
- Aksel Kaastrup Rasmussen (DTU), Denmark
- Rasmus Kleist Hørlyck Sørensen (DTU), Denmark
- Chao Zhang (DTU), Denmark
DTU: Technical University of Denmark, Department of Applied Mathematics and Computer Science Richard Petersens Plads Building 324 2800 Kgs. Lyngby Denmark
We built a complete electrode model CEM by extending the model provided in the linked BSc. thesis. We also built the framework to solve a nonlinear optimization problem of inferring the conductivity SMPrior provided by the competition. We used scipy L-BFGS-B to solve the optimization problem and segment with Chan-Vese segmentation method from scikit-image. Our implementation uses FEniCS, a finite element method library.
To run our EIT image reconstruction algorithm, you will need:
- Python 3.x
- Required Python libraries (listed in
requirements.txt) - Access to the provided dataset (not included in this repository)
Note that you will need to install FEniCS 2019.1.0. One way to do it is to follow Anaconda installation instructions in this link.
python main.py path/to/input/files path/to/output/files difficulty
| Phantom | Ref | Level 1 | Level 4 | Level 7 |
|---|---|---|---|---|
| a |  |
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| b |  |
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| c |  |
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| d |  |
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Scores for each phantom and difficulty 1,4 and 7:
| Phantom | Level 1 | Level 4 | Level 7 |
|---|---|---|---|
| a | 0.730 | 0.587 | 0.246 |
| b | 0.591 | 0.331 | 0.243 |
| c | 0.682 | 0.671 | 0.150 |
| d | 0.622 | 0.676 | 0.593 |
Scores have been computed using our own implementation of the scoring function based on scikit learn.
All files in the repository come with the Apache-v2.0 license unless differently specified.