Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomography
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Samara National Research University
2019-12-01
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| Series: | Компьютерная оптика |
| Subjects: | |
| Online Access: | http://computeroptics.smr.ru/KO/PDF/KO43-6/430611.pdf |
| _version_ | 1819163347354910720 |
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| author | Vitaly Vlasov Alexander Konovalov Sergey Kolchugin |
| author_facet | Vitaly Vlasov Alexander Konovalov Sergey Kolchugin |
| author_sort | Vitaly Vlasov |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T17:42:41Z |
| format | Article |
| id | doaj.art-b67e3783469b41529487e71ec536c9d6 |
| institution | Directory Open Access Journal |
| issn | 0134-2452 2412-6179 |
| language | English |
| last_indexed | 2024-12-22T17:42:41Z |
| publishDate | 2019-12-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj.art-b67e3783469b41529487e71ec536c9d62022-12-21T18:18:23ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792019-12-014361008102010.18287/2412-6179-2019-43-6-1008-1020Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomographyVitaly Vlasov0Alexander Konovalov1Sergey Kolchugin2Russian Federal Nuclear Center – Zababakhin Institute of Applied Physics, Chelyabinsk Region, Snezhinsk, 456770, Russia, 13 Vasiliev Str.Russian Federal Nuclear Center – Zababakhin Institute of Applied Physics, Chelyabinsk Region, Snezhinsk, 456770, Russia, 13 Vasiliev Str.Russian Federal Nuclear Center – Zababakhin Institute of Applied Physics, Chelyabinsk Region, Snezhinsk, 456770, Russia, 13 Vasiliev Str.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.http://computeroptics.smr.ru/KO/PDF/KO43-6/430611.pdffew-view tomographyimage reconstruction and segmentationcompressed sensingpotts functionaltotal variationshepp-logan phantomqr-codecorrelation coefficientdeviation factor |
| spellingShingle | Vitaly Vlasov Alexander Konovalov Sergey Kolchugin Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomography Компьютерная оптика few-view tomography image reconstruction and segmentation compressed sensing potts functional total variation shepp-logan phantom qr-code correlation coefficient deviation factor |
| title | Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomography |
| title_full | Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomography |
| title_fullStr | Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomography |
| title_full_unstemmed | Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomography |
| title_short | Joint image reconstruction and segmentation: Comparison of two algorithms for few-view tomography |
| title_sort | joint image reconstruction and segmentation comparison of two algorithms for few view tomography |
| topic | few-view tomography image reconstruction and segmentation compressed sensing potts functional total variation shepp-logan phantom qr-code correlation coefficient deviation factor |
| url | http://computeroptics.smr.ru/KO/PDF/KO43-6/430611.pdf |
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