DocumentCode :
2804906
Title :
Automatic detection of supraaortic branches and model-based segmentation of the aortic arch froM 3D CTA images
Author :
Biesdorf, A. ; Worz, Stefan ; von Tengg-Kobligk, H. ; Rohr, K.
Author_Institution :
Dept. Bioinf. & Functional Genomics, Univ. of Heidelberg, Heidelberg, Germany
fYear :
2009
fDate :
June 28 2009-July 1 2009
Firstpage :
486
Lastpage :
489
Abstract :
Automated quantification of the morphology of the aortic arch is crucial for diagnosis and treatment of cardiovascular diseases. We introduce a new approach for fully automatic segmentation and characterization of the aortic arch morphology for endovascular aortic repair. Supraaortic branches are detected based on an analysis of the connected components within a spherical volume around the vessel. Segmentation and quantification is based on a 3D parametric intensity model that is iteratively fitted to the image intensities and includes a fast and robust scheme for initialization. The performance of the approach has been evaluated using synthetic and real 3D CTA images.
Keywords :
angiocardiography; blood vessels; diagnostic radiography; image segmentation; medical image processing; 3D computed tomography angiography; 3D parametric intensity model; aortic arch morphology; automatic detection; endovascular aortic repair; fully automatic segmentation; supraaortic branches; Angiography; Bioinformatics; Biomedical imaging; Cancer; Cardiovascular diseases; Computer vision; Deformable models; Genomics; Image segmentation; Morphology; Aortic Arch Segmentation; Automatic Initialization; Branch Detection; Model-Based Segmentation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2009. ISBI '09. IEEE International Symposium on
Conference_Location :
Boston, MA
ISSN :
1945-7928
Print_ISBN :
978-1-4244-3931-7
Electronic_ISBN :
1945-7928
Type :
conf
DOI :
10.1109/ISBI.2009.5193090
Filename :
5193090
Link To Document :
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