Segmentation of Carotid Arteries By Graph-Cuts Using Centerline Models

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In this paper, we present a semi-automtic method for segmenting carotid arteries in contrast enhanced (CE)-CT angiography (CTA) scans.
The segmentation algorithm extracts the lumen of carotid arteries between user specfied locations. Specifically, the algorithm first
detects the centerline representations between the user placed seed points. This centerline extraction algorithm is based on a minimal path
detection algorithm which operates on a {em medialness} map. The lumen of corotid arteries is extracted by using
the global optimal graph-cuts algorithm~cite{boykov:01} using the centerlines as input. The radius information contained
in the centerline representation
is used to normalize the gradient based weights of the graph. It is shown that this algorithm can sucessfully segment the carotid arteries without
including calcified and non-calcified plaques in the segmentation results.

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Categories: Filtering, Segmentation
Keywords: graph-cuts, centerline extraction, medialness filters
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