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SAMPLING TYPE OPERATORS VERSUS AI IN MEDICAL IMAGE PROCESSING
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AI
DOI：10.3934/dcdss.2025147.png)
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Abstract
En 中文
. 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.
Keywords:
Sampling Kantorovich operators
convergence results
segmentation algorithm
image processing
U-Net neural network
similarity indices
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