Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/43471
Título: Segmentation of the aorta using active contours with histogram-based descriptors
Autores/as: Alemán-Flores, Miguel 
Santana-Cedrés, Daniel 
Alvarez, Luis 
Trujillo, Agustín 
Gómez, Luis 
Tahoces, Pablo G.
Carreira, José M.
Clasificación UNESCO: 220990 Tratamiento digital. Imágenes
120601 Construcción de algoritmos
120602 Ecuaciones diferenciales
120326 Simulación
32 Ciencias médicas
Palabras clave: Aorta
Segmentation
Active contours
CT
Fecha de publicación: 2018
Editor/a: Springer 
Proyectos: Nuevos Modelos Matemáticos Para la Segmentación y Clasificación en Imágenes 
Publicación seriada: Lecture Notes in Computer Science 
Conferencia: 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the 3rd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 
Resumen: This work presents an automatic method to segment the aortic lumen in computed tomography scans by combining an ellipse-based structure of the artery and an active contour model. The general shape of the aorta is first estimated by adapting the contour of its cross-sections to ellipses oriented in the direction orthogonal to the course of the vessel. From this set of ellipses, an initial segmentation is computed, which is used as starting approximation for the active contour technique. Apart from the traditional attraction and regularization terms of the active contours, an additional term is included to make the contour evolve according to the likelihood of a given intensity to be inside the aorta or in the surrounding tissues. With this technique, it is possible to adapt the boundary of the initial segmentation by considering not only the most significant edges, but also the distribution of the intensities inside and surrounding the aortic lumen.
URI: http://hdl.handle.net/10553/43471
ISBN: 978-3-030-01363-9
ISSN: 0302-9743
DOI: 10.1007/978-3-030-01364-6_4
Fuente: Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS 2018, CVII 2018, STENT 2018. Lecture Notes in Computer Science, v. 11043 LNCS, p. 28-35
Colección:Capítulo de libro
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