Mathematical models for the improvement of detection techniques of industrial noise sources from acoustic images

Asdrubali, Francesco; Baldinelli, Giorgio; Bianchi, Francesco; Costarelli, Danilo; D’Alessandro, Francesco; Scrucca, Flavio; Seracini, Marco; Vinti, Gianluca

doi:10.1002/mma.7420

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…Obviously, increasing w results in a better reconstruction of the original image. Moreover, the implementation of the SK operators acts also as a rescaling algorithm, so allowing the possibility of enhancing the original image, which is revealed to be very useful for the applications [3,[19][20][21]27].…”

Section: Approximation By Means Of Sampling Kantorovich Operatorsmentioning

confidence: 99%

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

et al. 2023

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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.

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Section: Approximation By Means Of Sampling Kantorovich Operatorsmentioning

confidence: 99%

“…Thanks to this implementation, several satisfactory results have been obtained, both in the biomedical and engineering fields (see, e.g., Refs. [20,21]).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“…Moreover, SK algorithm, based on the implementation of the well established theory of sampling Kantorovich (SK) operators [6][7][8][9] (see Section 2), has been proved not only to act as a low-pass filter, thus reducing noise, but also enjoying the property of increasing spatial resolution of images performing better than other well-known interpolation (nearest neighbor interpolation, bilinear and bicubic B-splines) and quasi-interpolation algorithms (quasi Finite Impulse Response "quasi FIR" and Infinite Impulse Response "quasi IIR") [10]. It plays a crucial role for the reconstruction and the enhancement of the images we will deal with, and it has been already employed for different issues belonging to both the medical and the engineering fields [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Alzheimer biomarkers esteem by sampling Kantorovich algorithm

Costarelli

Seracini

Travaglini

et al. 2023

Math Methods in App Sciences

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FundingInformation D. Costarelli, A. Travaglini, and G. Vinti have been partially supported within the 2022 GNAMPA-INdAM Project "Enhancement e segmentazione di immagini mediante operatori di tipo campionamento e metodi variazionali per lo studio di applicazioni biomediche" while G. Vinti within the projects: (1) Ricerca di Base 2019 dell'Università degli Studi di Perugia -"Integrazione, Approssimazione, Analisi Nonlineare e loro Applicazioni"; (2) "Metodi e processi innovativi per lo sviluppo di una banca di immagini mediche per fini diagnostici (B.I.M.)" funded by the Fondazione Cassa di Risparmio di Perugia (FCRP), 2018; and (3) "Metodiche di Imaging non invasivo mediante angiografia OCT sequenziale per lo studio delle Retinopatie degenerative dell'Anziano (M.I.R.A.)," funded by FCRP, 2019. In this paper, we take advantage of the reconstruction properties of the sampling Kantorovich (SK) algorithm to estimate the volume of the human brain for the quantification of Alzheimer's biomarkers. At first, the goodness of the reconstructions is evaluated, comparing it to different interpolation methods by means of the Peak Signal to Noise Ratio (PSNR) index; then the stereological Cavalieri/Point-counting technique is used to infer volumetric data starting from the knowledge of the planar sections. The comparison of the achieved results with synthetic references confirms the good performances of the new methodology.

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A Mathematical Model for the Analysis of Eye Fundus Images in Healthy and Diabetic Patients

Travaglini

Vinti

2023

Lecture Notes in Computer Science

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Mathematical models for the improvement of detection techniques of industrial noise sources from acoustic images

Cited by 5 publications

References 29 publications

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

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

Alzheimer biomarkers esteem by sampling Kantorovich algorithm

A Mathematical Model for the Analysis of Eye Fundus Images in Healthy and Diabetic Patients

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