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Automatic Branching Detection in IVUS Sequences

  • Marina Alberti
  • Carlo Gatta
  • Simone Balocco
  • Francesco Ciompi
  • Oriol Pujol
  • Joana Silva
  • Xavier Carrillo
  • Petia Radeva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)

Abstract

Atherosclerosis is a vascular pathology affecting the arterial walls, generally located in specific vessel sites, such as bifurcations. In this paper, for the first time, a fully automatic approach for the detection of bifurcations in IVUS pullback sequences is presented. The method identifies the frames and the angular sectors in which a bifurcation is visible. This goal is achieved by applying a classifier to a set of textural features extracted from each image of an IVUS pullback. A comparison between two state-of-the-art classifiers is performed, AdaBoost and Random Forest. A cross-validation scheme is applied in order to evaluate the performances of the approaches. The obtained results are encouraging, showing a sensitivity of 75% and an accuracy of 94% by using the AdaBoost algorithm.

Keywords

Percutaneous Coronary Intervention Local Binary Pattern IVUS Image Rigid Registration Longitudinal View 
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

  • Marina Alberti
    • 1
    • 2
  • Carlo Gatta
    • 1
    • 2
  • Simone Balocco
    • 1
    • 2
  • Francesco Ciompi
    • 1
    • 2
  • Oriol Pujol
    • 1
    • 2
  • Joana Silva
    • 3
  • Xavier Carrillo
    • 4
  • Petia Radeva
    • 1
    • 2
  1. 1.Dep. of Applied Mathematics and AnalysisUniversity of BarcelonaSpain
  2. 2.Computer Vision CenterCampus UABBarcelonaSpain
  3. 3.Cardiology DepartmentCoimbra’s Hospital CenterCoimbraPortugal
  4. 4.Unitat d’hemodinàmica cardíacaHospital universitari “Germans Trias i Pujol”BadalonaSpain

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