Automatic Vessel Segmentation from Pulsatile Radial Distension

  • Alborz Amir-Khalili
  • Ghassan Hamarneh
  • Rafeef Abugharbieh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9351)


Identification of vascular structures from medical images is integral to many clinical procedures. Most vessel segmentation techniques ignore the characteristic pulsatile motion of vessels in that formulation. In a recent effort to automatically segment vessels that are hidden under fat, we motivated the use of the magnitude of local pulsatile motion extracted from surgical endoscopic video. In this paper we propose a new approach that leverages the local orientation, in addition to magnitude of motion, and demonstrate that the extended computation of motion vectors can improve the segmentation of vascular structures. We implement our approach using two alternatives to magnitude-only motion estimation by using traditional optical flow and by exploiting the monogenic signal for fast flow estimation. Our evaluations are conducted on both synthetic phantoms as well as real ultrasound data showing improved segmentation results (0.36 increase in DSC and 0.11 increase in AUC) with negligible change in computational performance.


Optical Coherence Tomography Motion Vector Local Orientation Vessel Segmentation Monogenic Signal 
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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Alborz Amir-Khalili
    • 1
  • Ghassan Hamarneh
    • 2
  • Rafeef Abugharbieh
    • 1
  1. 1.BiSICLUniversity of British ColumbiaVancouverCanada
  2. 2.Medical Image Analysis LabSimon Fraser UniversityBurnabyCanada

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