Journal of Digital Imaging

, Volume 26, Issue 5, pp 909–919 | Cite as

Automatic Classification of Left Ventricular Regional Wall Motion Abnormalities in Echocardiography Images Using Nonrigid Image Registration

  • Ahmad Shalbaf
  • Hamid Behnam
  • Zahra Alizade-Sani
  • Maryam Shojaifard


Identification and classification of left ventricular (LV) regional wall motion (RWM) abnormalities on echocardiograms has fundamental clinical importance for various cardiovascular disease assessments especially in ischemia. In clinical practice, this evaluation is still performed visually which is highly dependent on training and experience of the echocardiographers and therefore suffers from significant interobserver and intraobserver variability. This paper presents a new automatic technique, based on nonrigid image registration for classifying the RWM of LV in a three-point scale. In this algorithm, we register all images of one cycle of heart to a reference image (end-diastolic image) using a hierarchical parametric model. This model is based on an affine transformation for modeling the global LV motion and a B-spline free-form deformation transformation for modeling the local LV deformation. We consider image registration as a multiresolution optimization problem. Finally, a new regional quantitative index based on resultant parameters of the hierarchical transformation model is proposed for classifying RWM in a three-point scale. The results obtained by our method are quantitatively evaluated to those obtained by two experienced echocardiographers visually as gold standard on ten healthy volunteers and 14 patients (two apical views) and resulted in an absolute agreement of 83 % and a relative agreement of 99 %. Therefore, this diagnostic system can be used as a useful tool as well as reference visual assessment to classify RWM abnormalities in clinical evaluation.


Echocardiography images LV motion Nonrigid image registration 


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Copyright information

© Society for Imaging Informatics in Medicine 2013

Authors and Affiliations

  • Ahmad Shalbaf
    • 1
  • Hamid Behnam
    • 1
  • Zahra Alizade-Sani
    • 2
  • Maryam Shojaifard
    • 2
  1. 1.Department of Biomedical Engineering, School of Electrical EngineeringIran University of Science and TechnologyTehranIran
  2. 2.Rajaie Cardiovascular Medical & Research CenterTehran University of Medical ScienceTehranIran

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