Tensor Voting Extraction of Vessel Centerlines from Cerebral Angiograms

  • Yu Ding
  • Mircea Nicolescu
  • Dan Farmer
  • Yao Wang
  • George Bebis
  • Fabien ScalzoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)


The extraction of vessel centerlines from cerebral angiograms is a prerequisite for 2D-3D reconstruction and computational fluid dynamic (CFD) simulations. Many researchers have studied vessel segmentation and centerline extraction on retinal images while less attention and efforts have been devoted to cerebral angiography images. Since cerebral angiograms consist of vessels that are much noisier because of the possible patient movement, it is often a more challenging task compared to working on retinal images. In this study, we propose a multi-scale tensor voting framework to extract the vessel centerlines from cerebral angiograms. The developed framework is evaluated on a dataset of routinely acquired angiograms and reach an accuracy of 91.75\(\%\pm 5.07\%\) during our experiments.


Digital Subtraction Angiography Noise Removal Main Vessel Vessel Segmentation Ground Truth Image 
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.



Prof. Scalzo was partially supported by a AHA grant 16BGIA27760152, a Spitzer grant, and received hardware donations from Gigabyte, Nvidia, and Intel.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yu Ding
    • 1
  • Mircea Nicolescu
    • 2
  • Dan Farmer
    • 2
  • Yao Wang
    • 1
  • George Bebis
    • 2
  • Fabien Scalzo
    • 1
    Email author
  1. 1.Department of NeurologyUniversity of California, Los Angeles (UCLA)Los AngelesUSA
  2. 2.Department of Computer ScienceUniversity of Nevada, Reno (UNR)RenoUSA

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