Automatic Coronary Tree Modeling
Siemens Corporate Research
| Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/1426 |
Submitted by Huseyin Tek on 07-08-2008.
In this paper, we present an automatic method for extracting center axis representations (centerlines)
of coronary arteries in contrast enhanced (CE)-CT angiography scans.
The algorithm first detects the aorta which is used as an initial mask for ostia
detection~cite{grady2006:fast}. Second, the ostia locations are detected via a vessel centerline extraction method~cite{Gulsun:Tek:008} which
tracks the center axis of the coronaries starting from the aorta surface. The full centerline tree of the coronary arteries are
computed via the multi-scale medialness-based vessel tree extraction algorithm which starts a tracking process from
the ostia locations until all the braches are reached. The centerline extraction algorithm is a
graph-based optimization algorithm using multi-scale medialness filters.
of coronary arteries in contrast enhanced (CE)-CT angiography scans.
The algorithm first detects the aorta which is used as an initial mask for ostia
detection~cite{grady2006:fast}. Second, the ostia locations are detected via a vessel centerline extraction method~cite{Gulsun:Tek:008} which
tracks the center axis of the coronaries starting from the aorta surface. The full centerline tree of the coronary arteries are
computed via the multi-scale medialness-based vessel tree extraction algorithm which starts a tracking process from
the ostia locations until all the braches are reached. The centerline extraction algorithm is a
graph-based optimization algorithm using multi-scale medialness filters.
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| Categories: | Edge Detection, Feature extraction, Filtering, Optimization, Segmentation, Surface extraction |
| Keywords: | segmentation, |
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