A Large Scale Analysis for Testing a Mathematical Model for the Study of Vascular Pathologies

Arianna Travaglini, Gianluca Vinti (), Giovanni Battista Scalera and Michele Scialpi
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Arianna Travaglini: Department of Mathematics and Computer Science, University of Perugia, 1, Via Vanvitelli, 06123 Perugia, Italy
Gianluca Vinti: Department of Mathematics and Computer Science, University of Perugia, 1, Via Vanvitelli, 06123 Perugia, Italy
Giovanni Battista Scalera: Division of Diagnostic Imaging, Department of Medicine and Surgery, University of Perugia, Santa Maria della Misericordia Hospital, 3, Piazzale Giorgio Menghini, 06129 Perugia, Italy
Michele Scialpi: Division of Diagnostic Imaging, Department of Medicine and Surgery, University of Perugia, Santa Maria della Misericordia Hospital, 3, Piazzale Giorgio Menghini, 06129 Perugia, Italy

Mathematics, 2023, vol. 11, issue 8, 1-19

Abstract: In this paper, we carry out a study developed on 13,677 images from 15 patients affected by moderate/severe atheromatous disease of the abdominal aortic tract. A procedure to extract the pervious lumen of the aorta artery from basal CT images is exploited and tested on a large scale. In particular, the above method takes advantage of the reconstruction and enhancing properties of the sampling Kantorovich algorithm which allows the information content of images to be increased. The processed image is compared, slice by slice, by superposition, with the corresponding contrast medium reference image. Numerical indices of errors were computed and analyzed in order to test the validity of the proposed method. The results achieved confirm, both from the numerical and clinical point of view, the good performance and accuracy of the proposed method, opening the possibility to perform an assisted diagnosis avoiding the injection of the contrast medium.

Keywords: sampling Kantorovich operators; approximation results; digital image processing; similarity indices; computed tomography; abdominal aortic aneurysm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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