Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomography
| Autor: | Vitaly Vlasov, Alexander Konovalov, Sergey Kolchugin |
|---|---|
| Jazyk: | English<br />Russian |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Компьютерная оптика, Vol 43, Iss 6, Pp 1008-1020 (2019) |
| Druh dokumentu: | article |
| ISSN: | 2412-6179 0134-2452 |
| DOI: | 10.18287/2412-6179-2019-43-6-1008-1020 |
| Popis: | Two algorithms of few-view tomography are compared, specifically, the iterative Potts minimization algorithm (IPMA) and the algebraic reconstruction technique with TV-regularization and adaptive segmentation (ART-TVS). Both aim to reconstruct piecewise-constant structures, use the compressed sensing theory, and combine image reconstruction and segmentation procedures. Using a numerical experiment, it is shown that either algorithm can exactly reconstruct the Shepp-Logan phantom from as small as 7 views with noise characteristic of the medical applications of X-ray tomography. However, if an object has a complicated high-frequency structure (QR-code), the minimal number of views required for its exact reconstruction increases to 17–21 for ART-TVS and to 32–34 for IPMA. The ART-TVS algorithm developed by the authors is shown to outperform IPMA in reconstruction accuracy and speed and in resistance to abnormally high noise as well. ART-TVS holds good potential for further improvement. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
|