Please use this identifier to cite or link to this item:
Title: Segmentation of the aorta using active contours with histogram-based descriptors
Authors: Alemán-Flores, Miguel 
Santana-Cedrés, Daniel 
Alvarez, Luis 
Trujillo, Agustín 
Gómez, Luis 
Tahoces, Pablo G.
Carreira, José M.
UNESCO Clasification: 220990 Tratamiento digital. Imágenes
120601 Construcción de algoritmos
120602 Ecuaciones diferenciales
120326 Simulación
32 Ciencias médicas
Keywords: Aorta
Active contours
Issue Date: 2018
Publisher: Springer 
Project: Nuevos Modelos Matemáticos Para la Segmentación y Clasificación en Imágenes 
Journal: Lecture Notes in Computer Science 
Conference: 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 
Abstract: 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.
ISBN: 978-3-030-01363-9
ISSN: 0302-9743
DOI: 10.1007/978-3-030-01364-6_4
Source: 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
Appears in Collections:Capítulo de libro
Adobe PDF (2,93 MB)
Show full item record

Google ScholarTM




Export metadata

Items in accedaCRIS are protected by copyright, with all rights reserved, unless otherwise indicated.