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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 Segmentation Active contours CT |
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. | 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 | 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 |
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