Szczegóły publikacji
Opis bibliograficzny
Enhanced CT image reconstruction using VMD-based Quaternion Bilateral Filtering / Mahmoud NASR, Krzysztof Brzostowski, Adam PIÓRKOWSKI // W: Computational Collective Intelligence : 17th International Conference, ICCCI 2025 : Ho Chi Minh City, Vietnam, November 12–15, 2025 : proceedings , Pt. 2 / eds. Ngoc Thanh Nguyen, [et al.]. — Cham : Springer Nature, cop. 2026. — ( Lecture Notes in Computer Science ; ISSN 0302-9743. Lecture Notes in Artificial Intelligence ; vol. 16139 ). — ISBN: 978-3-032-09320-2; e-ISBN: 978-3-032-09321-9. — S. 273–287. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-11-08. — M. Nasr - dod. afiliacja: Sano Centre for Computational Medicine, Kraków, Poland
Autorzy (3)
- AGHNasr Mahmoud Ahmed
- Brzostowski Krzysztof
- AGHPiórkowski Adam
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 164740 |
|---|---|
| Data dodania do BaDAP | 2025-12-09 |
| DOI | 10.1007/978-3-032-09321-9_19 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Computed tomography (CT) imaging is essential in medical diagnosis, with image quality heavily dependent on the selection of reconstruction kernels. Sharp kernels improve spatial resolution but increase noise, while soft kernels diminish noise at the expense of edge definition. This paper introduces a unique Variational Mode Decomposition with Quaternion Bilateral Filtering (VMD-QBF) method to convert sharp-kernel CT images into soft-kernel versions while maintaining critical structural information. The suggested method is assessed in comparison to conventional denoising techniques, such as Non-Local Means, Anisotropic Diffusion, Bilateral Filtering, and Quaternion Bilateral Filtering (QBF), utilizing various reconstruction kernels (B50, B46, B41, B36, B35, B31). The evaluation is performed with Mean Squared Error (MSE), Structural Similarity Index (SSIM), Multiscale SSIM (MS-SSIM), and Peak Signal-to-Noise Ratio (PSNR). Experimental findings indicate that VMD-QBF surpasses traditional filtering methods, attaining minimal MSE and maximal PSNR, while preserving enhanced structural similarity across all evaluated kernels. The results validate the efficacy of the suggested strategy in reducing noise while maintaining essential image characteristics, positioning it as a viable solution for post-reconstruction CT image enhancement.
