Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging

  • Hanne Kause
  • Aura Hernàndez-Sabaté
  • Patricia Márquez-Valle
  • Andrea Fuster
  • Luc Florack
  • Hans van Assen
  • Debora Gil
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9534)


Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm.



Work supported by Spanish project TIN2012-33116. First author is supported by the Dutch Technology Foundation STW, which is part of the Netherlands Organisation for Scientic Research (NWO), and which is partly funded by the Ministry of Economic Affairs. Third author is supported by the FPI-MICINN BES-2010-031102 program. Last author is a Serra Hunter fellow.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Hanne Kause
    • 1
  • Aura Hernàndez-Sabaté
    • 4
  • Patricia Márquez-Valle
    • 4
  • Andrea Fuster
    • 2
    • 3
  • Luc Florack
    • 2
    • 3
  • Hans van Assen
    • 1
  • Debora Gil
    • 4
  1. 1.Department of Electrical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  2. 2.Department of Biomedical EngineeringEindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.Department of Mathematics and Computer ScienceEindhoven University of TechnologyEindhovenThe Netherlands
  4. 4.Computer Vision Center and Computer Science DepartmentUniversitat Autònoma de BarcelonaBellaterra, BarcelonaSpain

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