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Proceedings Paper

Morphology-based three-dimensional segmentation of coronary artery tree from CTA scans
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Paper Abstract

We developed an algorithm based on a rule-based threshold framework to segment the coronary arteries from angiographic computed tomography (CTA) data. Computerized segmentation of the coronary arteries is a challenging procedure due to the presence of diverse anatomical structures surrounding the heart on cardiac CTA data. The proposed algorithm incorporates various levels of image processing and organ information including region, connectivity and morphology operations. It consists of three successive stages. The first stage involves the extraction of the three-dimensional scaffold of the heart envelope. This stage is semiautomatic requiring a reader to review the CTA scans and manually select points along the heart envelope in slices. These points are further processed using a surface spline-fitting technique to automatically generate the heart envelope. The second stage consists of segmenting the left heart chambers and coronary arteries using grayscale threshold, size and connectivity criteria. This is followed by applying morphology operations to further detach the left and right coronary arteries from the aorta. In the final stage, the 3D vessel tree is reconstructed and labeled using an Isolated Connected Threshold technique. The algorithm was developed and tested on a patient coronary artery CTA that was graciously shared by the Department of Radiology of the Massachusetts General Hospital. The test showed that our method constantly segmented the vessels above 79% of the maximum gray-level and automatically extracted 55 of the 58 coronary segments that can be seen on the CTA scan by a reader. These results are an encouraging step toward our objective of generating high resolution models of the male and female heart that will be subsequently used as phantoms for medical imaging system optimization studies.

Paper Details

Date Published: 8 March 2007
PDF: 11 pages
Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65122I (8 March 2007); doi: 10.1117/12.710122
Show Author Affiliations
Diem Phuc T. Banh, U.S. Food and Drug Administration (United States)
Iacovos S. Kyprianou, U.S. Food and Drug Administration (United States)
Sophie Paquerault, U.S. Food and Drug Administration (United States)
Kyle J. Myers, U.S. Food and Drug Administration (United States)


Published in SPIE Proceedings Vol. 6512:
Medical Imaging 2007: Image Processing
Josien P. W. Pluim; Joseph M. Reinhardt, Editor(s)

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