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Combining Growcut and Temporal Correlation for IVUS Lumen Segmentation

  • Simone Balocco
  • Carlo Gatta
  • Francesco Ciompi
  • Oriol Pujol
  • Xavier Carrillo
  • Josepa Mauri
  • Petia Radeva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)

Abstract

The assessment of arterial luminal area, performed by IVUS analysis, is a clinical index used to evaluate the degree of coronary artery disease. In this paper we propose a novel approach to automatically segment the vessel lumen, which combines model-based temporal information extracted from successive frames of the sequence, with spatial classification using the Growcut algorithm. The performance of the method is evaluated by an in vivo experiment on 300 IVUS frames. The automatic and manual segmentation performances in general vessel and stent frames are comparable. The average segmentation error in vessel, stent and bifurcation frames are 0.17±0.08 mm, 0.18±0.07 mm and 0.31±0.12 mm respectively.

Keywords

Temporal Correlation Successive Frame IVUS Image Frame Selection Propose Segmentation Method 
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

  • Simone Balocco
    • 1
    • 2
  • Carlo Gatta
    • 1
    • 2
  • Francesco Ciompi
    • 1
    • 2
  • Oriol Pujol
    • 1
    • 2
  • Xavier Carrillo
    • 3
  • Josepa Mauri
    • 3
  • Petia Radeva
    • 1
    • 2
  1. 1.Computer Vision CenterSpain
  2. 2.Dept. Matemàtica Aplicada i AnàlisiUniversitat de BarcelonaBarcelonaSpain
  3. 3.Hospital universitari Germans Trias i Pujol BadalonaSpain

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