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Automated segmentation of thoracic aorta in non-contrast CT images

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3 Author(s)
Uday Kurkure ; Computational Biomedicine Lab, University of Houston, TX, U.S.A. ; Olga C. Avila-Montes ; Ioannis A. Kakadiaris

Aortic calcification has been shown to be related to cardiovascular disease. In this paper, we present a novel method for localization and segmentation of thoracic aorta in non- contrast CT images using dynamic programming concepts to detect and quantify aortic calcium. The localization and segmentation of the aorta are formulated as optimal path detection problems, which are solved using dynamic programming principles. We apply these methods on Hough space for aorta localization and a transformed polar coordinate space for aorta segmentation. We evaluate the proposed approach by comparing it with the manual annotations in terms of aorta location, boundary distance, and volume overlap.

Published in:

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Date of Conference:

14-17 May 2008