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Proceedings Paper

An adaptive 3D region growing algorithm to automatically segment and identify thoracic aorta and its centerline using computed tomography angiography scans
Author(s): F. Ferreira; J. Dehmeshki; H. Amin; M. E. Dehkordi; A. Belli; A. Jouannic; S. Qanadli
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Paper Abstract

Thoracic Aortic Aneurysm (TAA) is a localized swelling of the thoracic aorta. The progressive growth of an aneurysm may eventually cause a rupture if not diagnosed or treated. This necessitates the need for an accurate measurement which in turn calls for the accurate segmentation of the aneurysm regions. Computer Aided Detection (CAD) is a tool to automatically detect and segment the TAA in the Computer tomography angiography (CTA) images. The fundamental major step of developing such a system is to develop a robust method for the detection of main vessel and measuring its diameters. In this paper we propose a novel adaptive method to simultaneously segment the thoracic aorta and to indentify its center line. For this purpose, an adaptive parametric 3D region growing is proposed in which its seed will be automatically selected through the detection of the celiac artery and the parameters of the method will be re-estimated while the region is growing thorough the aorta. At each phase of region growing the initial center line of aorta will also be identified and modified through the process. Thus the proposed method simultaneously detect aorta and identify its centerline. The method has been applied on CT images from 20 patients with good agreement with the visual assessment by two radiologists.

Paper Details

Date Published: 9 March 2010
PDF: 9 pages
Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76242T (9 March 2010); doi: 10.1117/12.844220
Show Author Affiliations
F. Ferreira, Kingston Univ. (United Kingdom)
J. Dehmeshki, Kingston Univ. (United Kingdom)
H. Amin, MedIAR Ltd. (United Kingdom)
M. E. Dehkordi, MedIAR Ltd. (United Kingdom)
A. Belli, St. George's Univ. of London (United Kingdom)
A. Jouannic, Univ. de Lausanne (Switzerland)
S. Qanadli, Univ. de Lausanne (Switzerland)

Published in SPIE Proceedings Vol. 7624:
Medical Imaging 2010: Computer-Aided Diagnosis
Nico Karssemeijer; Ronald M. Summers, Editor(s)

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