Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/40192
Title: Robust detection of circles in the vessel contours and application to local probability density estimation
Authors: Alvarez, Luis 
González Sánchez, Esther 
Esclarín, Julio 
Gomez, Luis 
Alemán-Flores, Miguel 
Trujillo, Agustín 
Cuenca Hernández, Carmelo 
Mazorra, Luis 
Tahoces, P.G.
Carreira-Villamor, José Martín
UNESCO Clasification: 220990 Tratamiento digital. Imágenes
120601 Construcción de algoritmos
120602 Ecuaciones diferenciales
120326 Simulación
Keywords: Circle Hough transform
CT images
Histogram analysis
Seed point
Vessels
Issue Date: 2017
Publisher: Springer
Project: Nuevos Modelos Matemáticos Para la Segmentación y Clasificación en Imágenes 
Journal: Lecture Notes in Computer Science 
Conference: 6th Joint International Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT0 / 2nd International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (LABELS)
Abstract: In this work we propose a technique to automatically estimate circular cross-sections of the vessels in CT scans. First, a circular contour is extracted for each slice of the CT by using the Hough transform. Afterward, the locations of the circles are optimized by means of a parametric snake model, and those circles which best fit the contours of the vessels are selected by applying a robust quality criterion. Finally, this collection of circles is used to estimate the local probability density functions of the image intensity inside and outside the vessels. We present a large variety of experiments on CT scans which show the reliability of the proposed method.
URI: http://hdl.handle.net/10553/40192
ISBN: 978-3-319-67533-6
ISSN: 0302-9743
DOI: 10.1007/978-3-319-67534-3_1
Source: Intravascular Imaging and Computer Assisted Stenting, and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis. LABELS 2017, STENT 2017, CVII 2017. Lecture Notes in Computer Science, v. 10552 LNCS, p. 3-11
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