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Joint Thrombus and Vessel Segmentation Using Dynamic Texture Likelihoods and Shape Prior

  • Nicolas Brieu
  • Martin Groher
  • Jovana Serbanovic-Canic
  • Ana Cvejic
  • Willem Ouwehand
  • Nassir Navab
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6893)

Abstract

The segmentation of thrombus and vessel in microscopic image sequences is of high interest for identifying genes linked to cardiovascular diseases. This task is however challenging because of the low contrast and the highly dynamic conditions observed in time-lapse DIC in-vivo microscopic scenes. In this work, we introduce a probabilistic framework for the joint segmentation of thrombus and vessel regions. Modeling the scene with dynamic textures, we derive two likelihood functions to account for both spatial and temporal discrepancies of the motion patterns. A tubular shape prior is moreover introduced to constrain the aortic region. Extensive experiments on microscopic sequences quantitatively show the good performance of our approach.

Keywords

Collateral Vessel Dynamic Texture Motion Segmentation Vessel Segmentation Tubular Shape 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nicolas Brieu
    • 1
  • Martin Groher
    • 1
  • Jovana Serbanovic-Canic
    • 2
    • 3
  • Ana Cvejic
    • 2
    • 3
  • Willem Ouwehand
    • 2
    • 3
    • 4
  • Nassir Navab
    • 1
  1. 1.Computer Aided Medical ProceduresTechnische Universität MünchenGermany
  2. 2.The Wellcome Trust Sanger InstituteHinxtonUK
  3. 3.Department of HematologyUniversity of CambridgeUK
  4. 4.NHS Blood and TransplantCambridgeUK

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