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    Semi-Automatic 3D Reconstruction of Atheroma Plaques from Intravascular Ultrasound Images Using an ad-hoc Algorithm

    Autor: 
    Martínez, Javier
    ;
    Pérez-Palau, Daniel
    ;
    Cilla, Myriam
    ;
    Garrido, Neus
    ;
    Larrañaga, Ane
    ;
    Pérez-Rey, Ignacio
    Fecha: 
    2023
    Palabra clave: 
    3-D reconstruction; atheroma plaque; image processing; IVUS; Scopus; JCR
    Revista / editorial: 
    Mathematics
    Citación: 
    Martínez, J., Pérez-Palau, D., Cilla, M., Garrido, N., Larrañaga, A., & Pérez-Rey, I. (2023). Semi-Automatic 3D Reconstruction of Atheroma Plaques from Intravascular Ultrasound Images Using an ad-hoc Algorithm. Mathematics, 11(3), 537. MDPI AG. Retrieved from http://dx.doi.org/10.3390/math11030537
    Tipo de Ítem: 
    Articulo Revista Indexada
    URI: 
    https://reunir.unir.net/handle/123456789/15419
    DOI: 
    https://doi.org/10.3390/math11030537
    Dirección web: 
    https://www.mdpi.com/2227-7390/11/3/537
    Open Access
    Resumen:
    The occurrence of atheroma plaques in the arteries can eventually obstruct them, leading to diseases such as atherosclerosis, which can cause, among others, a myocardial infarction or a stroke. As a consequence, it is necessary to shorten the time spent in locating and reconstructing the atheroma plaque that can be developed in an artery. This localization is usually conducted manually from the contours located on the cross-sectional radiographs of the artery and then reconstructed by creating the volumes using different techniques. This paper presents a 3-D reconstruction of the atheroma plaque by applying an image processing algorithm ad-hoc developed in order to obtain the boundaries of the atheroma, from a set of intravascular ultrasound images. The advantage of the approach developed in this paper is that it can be implemented in common medical procedures, as an important complementary decision-support tool. By reconstructing the atheroma instead of the artery, this work provides a different approach to improve its location and treatment. Results presented herein can be implemented in machine-learning-based algorithms, able to predict the growth and extent of incipient atheroma plaques, which ultimately contribute to an early detection of this pathology.
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