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ECOC Random Fields for Lumen Segmentation in Radial Artery IVUS Sequences

  • Francesco Ciompi
  • Oriol Pujol
  • Eduard Fernández-Nofrerías
  • Josepa Mauri
  • Petia Radeva
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5762)

Abstract

The measure of lumen volume on radial arteries can be used to evaluate the vessel response to different vasodilators. In this paper, we present a framework for automatic lumen segmentation in longitudinal cut images of radial artery from Intravascular ultrasound sequences. The segmentation is tackled as a classification problem where the contextual information is exploited by means of Conditional Random Fields (CRFs). A multi-class classification framework is proposed, and inference is achieved by combining binary CRFs according to the Error-Correcting-Output-Code technique. The results are validated against manually segmented sequences. Finally, the method is compared with other state-of-the-art classifiers.

Keywords

Support Vector Machine Radial Artery Local Binary Pattern Conditional Random Field Lumen Area 
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 2009

Authors and Affiliations

  • Francesco Ciompi
    • 1
    • 2
  • Oriol Pujol
    • 1
    • 2
  • Eduard Fernández-Nofrerías
    • 3
  • Josepa Mauri
    • 3
  • Petia Radeva
    • 1
    • 2
  1. 1.Dep. of Applied Mathematics and AnalysisUniversity of BarcelonaSpain
  2. 2.Computer Vision CenterBellaterraSpain
  3. 3.University Hospital “Germans Trias i Pujol”BadalonaSpain

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