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Model-Guided Extraction of Coronary Vessel Structures in 2D X-Ray Angiograms

  • Shih-Yu Sun
  • Peng Wang
  • Shanhui Sun
  • Terrence Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Analysis of vessel structures in 2D X-ray angiograms is important for pre-operative evaluation and image-guided intervention. However, automated vessel segmentation in angiograms, especially extraction of the topology such as bifurcations and vessel crossings, remains challenging mainly due to the projective nature of angiography and background clutter. In this paper, a novel framework for model-guided coronary vessel extraction in 2D angiograms is presented. In this framework, a graph is constructed using a sparse set of pixels in the angiogram. With a single user-supplied click as the starting point, the vessel tree structure in the angiogram is automatically extracted from the graph. Ambiguities in this tree structure caused by 3D-to-2D projection are then resolved using topological information from the 3D vessel model of the same patient. By incorporating this prior shape information, the proposed method is effective in extraction of vessel topology, and is robust to background clutter and uneven illumination. Through quantitative evaluation on 20 angiograms, it is shown that this model-guided approach significantly improves detection of vessel structures and bifurcations.

Keywords

Compute Tomography Angiography Vessel Segment Background Clutter Vessel Structure Vessel Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shih-Yu Sun
    • 1
  • Peng Wang
    • 1
  • Shanhui Sun
    • 1
  • Terrence Chen
    • 1
  1. 1.Siemens Corporation, Corporate TechnologyPrincetonUSA

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