Name
Title | ||
|---|---|---|
Deep Neural Networks for Ring Artifacts Segmentation and Corrections in Fragments of CT Images |
Abstract | ||
|---|---|---|
Ring artifacts are typical defects of computed tomography (CT) that degrade the quality of a 3D reconstructed image. Existing techniques for a ring reduction have various shortcomings and limitations, in particular, a lot of them are unable to process arbitrary fragments of the image and blur artifact-free regions. We propose an algorithm for ring artifacts segmentation and reduction by deep convolutional neural networks that correct 3D fragments of the CT image by inpainting. We compare 2D and 3D architectures of networks. For the creation of a dataset with a big number of ring artifacts, we propose a procedure that is able to transfer an artifact from one image to an arbitrary place of another image. The appearance of the transferred artifact changes. For ring artifact segmentation and correction in images of sandstones and sand, the proposed networks demonstrate good visual results and outperform existing methods. The proposed technique concentrates on the Digital Rock workflow, but the networks can be adjusted for the processing of other CT images as well. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.23919/FRUCT50888.2021.9347587 | 2021 28th Conference of Open Innovations Association (FRUCT) |
Keywords | DocType | ISSN |
3D architectures,transferred artifact changes,ring artifact segmentation,CT image,deep neural networks,ring artifacts segmentation,3D reconstructed image,ring reduction,arbitrary fragments,artifact-free regions,deep convolutional neural networks | Conference | 2305-7254 |
ISBN | Citations | PageRank |
978-1-7281-9107-2 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anton S. Kornilov | 1 | 0 | 0.68 |
| Ilia Safonov | 2 | 0 | 0.68 |
| Iryna Reimers | 3 | 0 | 0.34 |
| Ivan Yakimchuk | 4 | 0 | 1.01 |