Moment preserving tomographic image reconstruction model(Article)
- aFaculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
- bDepartment of Image Processing and Computer Graphics, University of Szeged, Szeged, Hungary
Abstract
Shape descriptors provide valuable prior information in many tomographic image reconstruction methods. Such descriptors include, among others, centroid, circularity, orientation, and elongation. Shape descriptor measures are often analytically expressed as a composition of certain geometric moments. Building upon this fact, this paper suggests preserving the values of a specific geometric moment in the reconstruction process, instead of preserving entire descriptors, as it has been suggested so far. Reconstructions from two natural projection directions (vertical and horizontal) are considered with special attention. The provided theoretical analysis demonstrates that preserving the value of a specific geometric moment, provided as prior information for the reconstruction process, simultaneously ensures the preservation of the true measures of all four abovementioned descriptors. Based on this result, a novel regularized energy minimization reconstruction model is proposed. The minimization task of the new model is solved using gradient-based optimization algorithm. Performance evaluation of the proposed method is supported by experimental results obtained through comparisons with other well-known reconstruction methods. © 2024 Elsevier B.V.
Indexed keywords
| Engineering controlled terms: | GeometryHistoric preservationImage reconstructionPetroleum reservoir evaluation |
|---|---|
| Engineering uncontrolled terms | DescriptorsGeometric momentGradient-based optimizationImage momentsImage reconstruction methodsMoment preservingPrior informationReconstruction processShape descriptorsTomographic image reconstruction |
| Engineering main heading: | Tomography |
Funding details
| Funding sponsor | Funding number | Acronym |
|---|---|---|
| Nemzeti Kutatási, Fejlesztési és Innovaciós Alap | NKFIA | |
| 451-03-65/2024-03/200156 | ||
| 01-3394/1 | ||
| Magyar Tudományos Akadémia | TKP2021-NVA-09 | MTA |
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1
The research work of T. Luki\u0107 has been supported by the Ministry of Science, Technological Development and Innovation (Contract No. 451-03-65/2024-03/200156 ) and the Faculty of Technical Sciences, University of Novi Sad through project \u201CScientific and Artistic Research Work of Researchers in Teaching and Associate Positions at the Faculty of Technical Sciences, University of Novi Sad\u201D (No. 01-3394/1 ). He also acknowledges to the Domus Hungarica Scientiarum et Artium program of the Hungarian Academy of Sciences . The work of P\u00E9ter Bal\u00E1zs was supported by project no. TKP2021-NVA-09 that has been implemented with the support provided by the Ministry of Innovation and Technology of Hungary from the National Research, Development and Innovation Fund , financed under the TKP2021-NVA funding scheme.
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2
The research work of T. Luki\u0107 has been supported by the Ministry of Science, Technological Development and Innovation (Contract No. 451-03-65/2024-03/200156) and the Faculty of Technical Sciences, University of Novi Sad through project \u201CScientific and Artistic Research Work of Researchers in Teaching and Associate Positions at the Faculty of Technical Sciences, University of Novi Sad\u201D (No. 01-3394/1). He also acknowledges to the Domus Hungarica Scientiarum et Artium program of the Hungarian Academy of Sciences. The work of P\u00E9ter Bal\u00E1zs was supported by project no. TKP2021-NVA-09 that has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the TKP2021-NVA funding scheme.
- ISSN: 02628856
- CODEN: IVCOD
- Source Type: Journal
- Original language: English
- DOI: 10.1016/j.imavis.2024.105036
- Document Type: Article
- Publisher: Elsevier Ltd
Lukić, T.; Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia;
© Copyright 2024 Elsevier B.V., All rights reserved.