Szczegóły publikacji
Opis bibliograficzny
An approach for CT image conversion using filtering based on quaternion mathematics / Mahmoud NASR, Adam PIÓRKOWSKI, Krzysztof Brzostowski, Fathi E. Abd El-Samie // W: Progress on Pattern Classification, Image Processing and Communications : proceedings of the CORES [13th International Conference on Computer Recognition Systems] and IP&C [13th International Conference on Image Processing and Communications] Conferences 2023 : [June 28–29, 2023, Wrocław] / eds. Robert Burduk, [et al.]. — Cham : Springer, cop. 2023. — (Lecture Notes in Networks and Systems ; ISSN 2367-3370 ; LNNS 766). — ISBN: 978-3-031-41629-3; e-ISBN: 978-3-031-41630-9. — S. 145–156. — Bibliogr., Abstr. — Publikacja dostępna online od: 2023-12-01. — M. Nasr - dod. afiliacja: Engineering Mathematics and Physics Department, Faculty of Engineering and Technology, Future University in Egypt (FUE), Egypt
Autorzy (4)
- AGHNasr Mahmoud Ahmed
- AGHPiórkowski Adam
- Brzostowski Krzysztof
- Abd El-Samie Fathi E.
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 150831 |
|---|---|
| Data dodania do BaDAP | 2023-12-16 |
| DOI | 10.1007/978-3-031-41630-9_15 |
| Rok publikacji | 2023 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Czasopismo/seria | Lecture Notes in Networks and Systems |
Abstract
Computed tomography (CT) images are widely used in medical examination applications. They are constructed from the raw data using different kernels. However, due to the technology used, the software finds problems with image segmentation in the presence of noise or blurred edges. In this paper, we use quaternion mathematics for image de-noising by applying the bilateral filtering algorithm to reconstruct a pure soft image from the images reconstructed with sharp kernels, without using the raw data. Our output is compared to reconstructed images with different denoising techniques, such as non-local means and wavelet denoising. The reconstructed images using other techniques are assessed using structural index similarity (SSIM) and peak signal-to-noise ratio (PSNR). Results show high similarity by comparing the outputs of sharp kernels to that of the B30f. Furthermore, the results show that the proposed work can efficiently increase the similarity between the images reconstructed with hard and soft kernels.
