Identificador persistente para citar o vincular este elemento:
http://hdl.handle.net/10553/40298
Título: | Tracking the aortic lumen geometry by optimizing the 3D orientation of its cross-sections | Autores/as: | Alvarez, Luis Trujillo, Agustín Cuenca Hernández, Carmelo González Sánchez, Esther Esclarín, Julio Gomez, Luis Mazorra, Luis Alemán-Flores, Miguel Tahoces, P. G. Carreira-Villamor, José Martín |
Clasificación UNESCO: | 220990 Tratamiento digital. Imágenes 32 Ciencias médicas 120601 Construcción de algoritmos 120326 Simulación 120602 Ecuaciones diferenciales |
Palabras clave: | Aorta Centerline Cross-section CT Ellipse tracking |
Fecha de publicación: | 2017 | Editor/a: | Springer | Proyectos: | Nuevos Modelos Matemáticos Para la Segmentación y Clasificación en Imágenes | Publicación seriada: | Lecture Notes in Computer Science | Conferencia: | 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, (MICCAI 2017) | Resumen: | We propose a fast incremental technique to compute the 3D geometry of the aortic lumen from a seed point located inside it. Our approach is based on the optimization of the 3D orientation of the cross-sections of the aorta. The method uses a robust ellipse estimation algorithm and an energy-based optimization technique to automatically track the centerline and the cross sections. In order to perform the optimization, we consider the size and the eccentricity of the ellipse which best fit the contour of the aorta on each cross-sectional plane. The method works directly on the original image and does not require a prior segmentation of the aortic lumen. We present some preliminary results which show the accuracy of the method and its ability to cope with challenging real CT (computed tomography) images of aortic lumens with significant angulations due to severe elongations. | URI: | http://hdl.handle.net/10553/40298 | ISBN: | 978-3-319-66184-1 | ISSN: | 0302-9743 | DOI: | 10.1007/978-3-319-66185-8_20 | Fuente: | Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017. MICCAI 2017. Lecture Notes in Computer Science, v. 10434 LNCS, p. 174-181 |
Colección: | Capítulo de libro |
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