A Learning Framework for the Automatic and Accurate Segmentation of Cardiac Tagged MRI Images (original) (raw)

Abstract

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.

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Authors and Affiliations

  1. Center for Computational Biomedicine Imaging and Modeling (CBIM), Rutgers University, New Brunswick, New Jersey, USA
    Zhen Qian & Dimitris N. Metaxas
  2. Department of Radiology, New York University, New York, USA
    Leon Axel

Authors

  1. Zhen Qian
  2. Dimitris N. Metaxas
  3. Leon Axel

Editor information

Editors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
    Yanxi Liu
  2. National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100080, Beijing, People’s Republic of China
    Tianzi Jiang
  3. State Key Laboratory for Intelligent Technology and Systems, Department of Automation, Faculty of Information Science and Technology, Tsinghua University, 100084, Beijing, China
    Changshui Zhang

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© 2005 Springer-Verlag Berlin Heidelberg

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Qian, Z., Metaxas, D.N., Axel, L. (2005). A Learning Framework for the Automatic and Accurate Segmentation of Cardiac Tagged MRI Images. In: Liu, Y., Jiang, T., Zhang, C. (eds) Computer Vision for Biomedical Image Applications. CVBIA 2005. Lecture Notes in Computer Science, vol 3765. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11569541\_11

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