Artificial Enlargement of a Training Set for Statistical Shape Models: Application to Cardiac Images

  • J. Lötjönen
  • K. Antila
  • E. Lamminmäki
  • J. Koikkalainen
  • M. Lilja
  • T. Cootes
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3504)

Abstract

Different methods were evaluated to enlarge artificially a training set which is used to build a statistical shape model. In this work, the shape model was built from MR data of 25 subjects and it consisted of ventricles, atria and epicardium. The method adding smooth non-rigid deformations to original training set examples produced the best results. The results indicated also that artificial deformation modes model better an unseen object than an equal number of standard PCA modes generated from original data.

Keywords

Deformation Mode Segmentation Result Shape Model Manual Segmentation Normalize Mutual Information 
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

  • J. Lötjönen
    • 1
  • K. Antila
    • 1
  • E. Lamminmäki
    • 1
  • J. Koikkalainen
    • 2
  • M. Lilja
    • 2
  • T. Cootes
    • 3
  1. 1.VTT Information TechnologyTampereFinland
  2. 2.Laboratory of Biomedical EngineeringHelsinki University of Technology, HUTFinland
  3. 3.Division of Imaging Science and Biomedical EngineeringUniversity of ManchesterU.K

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