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Proceedings Paper

Automatic extraction of the pulmonary artery tree from multi-slice CT data
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Paper Abstract

The purpose of this paper is to present an automated method for the extraction of the pulmonary vessel tree from multi-slice CT data. Furthermore we investigate a method for the separation of pulmonary arteries from veins. The vessel tree extraction is performed by a seed-point based front-propagation algorithm. This algorithm is based on a similar methodology as the bronchial tree segmentation and coronary artery tree extraction methods presented at earlier SPIE conferences. Our method for artery/vein separation is based upon the fact that the pulmonary artery tree accompanies the bronchial tree. For each extracted vessel segment, we evaluate a measure of "arterialness". This measure combines two components: a method for identifying candidate positions for a bronchus running in the vicinity of a given vessel on the one hand and a co-orientation measure for the vessel segment and bronchus candidates. The latter component rewards vessels running parallel to a nearby bronchus. The spatial orientation of vessel segments and bronchi is estimated by applying the structure tensor to the local gray-value neighbourhood. In our experiments we used multi slice CT datasets of the lung acquired by Philips IDT 16-slice, and Philips Brilliance 40-slice scanners. It can be shown that the proposed measure reduces the number of pulmonary veins falsely included into the arterial tree.

Paper Details

Date Published: 14 April 2005
PDF: 11 pages
Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); doi: 10.1117/12.595286
Show Author Affiliations
Thomas Buelow, Philips Research Labs. (Germany)
Rafael Wiemker, Philips Research Labs. (Germany)
Thomas Blaffert, Philips Research Labs. (Germany)
Cristian Lorenz, Philips Research Labs. (Germany)
Steffen Renisch, Philips Research Labs. (Germany)


Published in SPIE Proceedings Vol. 5746:
Medical Imaging 2005: Physiology, Function, and Structure from Medical Images
Amir A. Amini; Armando Manduca, Editor(s)

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