Boosting and Nonparametric Based Tracking of Tagged MRI Cardiac Boundaries

  • Zhen Qian
  • Dimitris N. Metaxas
  • Leon Axel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4190)


In this paper we present an accurate cardiac boundary tracking method for 2D tagged MRI time sequences. This method naturally integrates the motion and the static local appearance features and generates accurate boundary criteria via a boosting approach. We extend the conventional Adaboost classifier into a posterior probability form, which can be embedded in a particle filtering-based shape tracking framework. To make the tracking process more robust and faster, we use a PCA subspace shape representation to constrain the shape variation and lower the dimensionality. We also learn two shape-dynamic models for systole and diastole separately, to predict the shape evolution. Our tracking method incorporates the static appearance, the motion appearance, the shape constraints, and the dynamic prediction in a unified way. The proposed method has been implemented on 50 tagged MRI sequences. The experimental results show the accuracy and robustness of our approach.


Right Ventricle Tracking Process Landmark Point Boundary Criterion Cusp Point 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhen Qian
    • 1
  • Dimitris N. Metaxas
    • 1
  • Leon Axel
    • 2
  1. 1.Center for Computational Biomedicine Imaging and ModelingRutgers UniversityNew BrunswickUSA
  2. 2.Department of RadiologyNew York UniversityNew YorkUSA

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