A Learning Framework for the Automatic and Accurate Segmentation of Cardiac Tagged MRI Images

  • Zhen Qian
  • Dimitris N. Metaxas
  • Leon Axel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)


In this paper we present a fully automatic and accurate segmentation framework for 2D tagged cardiac MR images. This scheme consists of three learning methods: a) an active shape model is implemented to model the heart shape variations, b) an Adaboost learning method is applied to learn confidence-rated boundary criterions from the local appearance features at each landmark point on the shape model, and c) an Adaboost detection technique is used to initialize the segmentation. The set of boundary statistics learned by Adaboost is the weighted combination of all the useful appearance features, and results in more reliable and accurate image forces compared to using only edge or region information. Our experimental results show that given similar imaging techniques, our method can achieve a highly accurate performance without any human interaction.


Right Ventricle Shape Model Learn Framework Image Force Landmark Point 
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 2005

Authors and Affiliations

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

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