Sampling type operators versus AI in medical image processing
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| Názov: | Sampling type operators versus AI in medical image processing |
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| Autori: | Rinelli, Lucrezia, Travaglini, Arianna, Vescera, Nicolò, Vinti, Gianluca |
| Prispievatelia: | Rinelli, Lucrezia, Travaglini, Arianna, Vescera, Nicolò, Vinti, Gianluca |
| Rok vydania: | 2025 |
| Zbierka: | IRIS Università degli Studi di Perugia |
| Predmety: | Sampling Kantorovich operators, convergence results, segmentation algorithm, image processing, U-Net neural network, similarity indices |
| Popis: | This paper presents a study that leverages the important and innovative approximation properties of sampling Kantorovich operators, implemented in the processing of computed tomography (CT) images of patients with abdominal aortic aneurysm (AAA). By exploiting the remarkable reconstruction capabilities of these operators, we investigate a deterministic model grounded in a segmentation algorithm where sampling Kantorovich operators play a crucial role. This mathematically-based approach is compared with an artificial intelligence-based method employing a U-Net neural network. Both methods aim to segment the patent area of the aortic vessel, offering innovative and alternative techniques to the nephrotoxic contrast agents, typically used in diagnosing AAA. The results obtained from testing both methods were evaluated numerically and visually, demonstrating that both approaches yield accurate outcomes. |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | English |
| Relation: | info:eu-repo/semantics/altIdentifier/wos/WOS:001593290900001; volume:0; issue:0; firstpage:0; lastpage:0; numberofpages:1; journal:DISCRETE AND CONTINUOUS DYNAMICAL SYSTEMS. SERIES S; https://hdl.handle.net/11391/1608335 |
| DOI: | 10.3934/dcdss.2025147 |
| Dostupnosť: | https://hdl.handle.net/11391/1608335 https://doi.org/10.3934/dcdss.2025147 https://www.aimsciences.org/article/doi/10.3934/dcdss.2025147 |
| Prístupové číslo: | edsbas.99569076 |
| Databáza: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | This paper presents a study that leverages the important and innovative approximation properties of sampling Kantorovich operators, implemented in the processing of computed tomography (CT) images of patients with abdominal aortic aneurysm (AAA). By exploiting the remarkable reconstruction capabilities of these operators, we investigate a deterministic model grounded in a segmentation algorithm where sampling Kantorovich operators play a crucial role. This mathematically-based approach is compared with an artificial intelligence-based method employing a U-Net neural network. Both methods aim to segment the patent area of the aortic vessel, offering innovative and alternative techniques to the nephrotoxic contrast agents, typically used in diagnosing AAA. The results obtained from testing both methods were evaluated numerically and visually, demonstrating that both approaches yield accurate outcomes. |
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| DOI: | 10.3934/dcdss.2025147 |