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Supervised Learning Modelization and Segmentation of Cardiac Scar in Delayed Enhanced MRI

  • Laura Lara
  • Sergio Vera
  • Frederic Perez
  • Nico Lanconelli
  • Rita Morisi
  • Bruno Donini
  • Dario Turco
  • Cristiana Corsi
  • Claudio Lamberti
  • Giovana Gavidia
  • Maurizio Bordone
  • Eduardo Soudah
  • Nick Curzen
  • James Rosengarten
  • John Morgan
  • Javier Herrero
  • Miguel A. González Ballester
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7746)

Abstract

Delayed Enhancement Magnetic Resonance Imaging can be used to non-invasively differentiate viable from non-viable myocardium within the Left Ventricle in patients suffering from myocardial diseases. Automated segmentation of scarified tissue can be used to accurately quantify the percentage of myocardium affected. This paper presents a method for cardiac scar detection and segmentation based on supervised learning and level set segmentation. First, a model of the appearance of scar tissue is trained using a Support Vector Machines classifier on image-derived descriptors. Based on the areas detected by the classifier, an accurate segmentation is performed using a segmentation method based on level sets.

Keywords

Myocardial Scar Support Vector Machine Level Set Segmentation 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Laura Lara
    • 1
  • Sergio Vera
    • 1
  • Frederic Perez
    • 1
  • Nico Lanconelli
    • 2
  • Rita Morisi
    • 2
  • Bruno Donini
    • 2
  • Dario Turco
    • 2
  • Cristiana Corsi
    • 2
  • Claudio Lamberti
    • 2
  • Giovana Gavidia
    • 3
  • Maurizio Bordone
    • 3
  • Eduardo Soudah
    • 3
  • Nick Curzen
    • 4
  • James Rosengarten
    • 4
  • John Morgan
    • 4
  • Javier Herrero
    • 1
  • Miguel A. González Ballester
    • 1
  1. 1.Alma IT SystemsBarcelonaSpain
  2. 2.Alma Mater StudiorumUniversity of BolognaItaly
  3. 3.Centre Internacional de Mètodes Numèrics en EnginyeriaBarcelonaSpain
  4. 4.University HospitalSouthamptonUK

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