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Digital image processing algorithms for diagnosis in arterial diseases
Author(s) -
Costarelli Danilo,
Seracini Marco,
Vinti Gianluca
Publication year - 2015
Publication title -
pamm
Language(s) - English
Resource type - Journals
ISSN - 1617-7061
DOI - 10.1002/pamm.201510324
Subject(s) - sampling (signal processing) , field (mathematics) , individuation , medicine , artificial intelligence , multivariate statistics , algorithm , computer science , radiology , mathematics , computer vision , machine learning , psychology , filter (signal processing) , psychoanalysis , pure mathematics
Computed Tomography images (C.T.) are currently part of the routine procedure in medical diagnostic techniques and can be used for the evaluation of occlusion rate of arterial vessels in presence of many diseases. The correct individuation of the morphology of these arterial anomalies allows specialists to diagnose the risk rate for the health of patients and to decide for surgical stent implants. Multivariate sampling Kantorovich operators are suitable for studying not necessarily continuos signals/images as the ones involved in the medical field. Convergent results for such a family of discrete operators translate into reconstruction and even enhancement of a given signal/image increasing the sampling rate. (© 2015 Wiley‐VCH Verlag GmbH & Co. KGaA, Weinheim)