Optimal Mean Shape for Nonrigid Object Detection and Segmentation

  • Yefeng Zheng
  • Dorin Comaniciu
Chapter
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Abstract

To improve the shape initialization accuracy, this chapter presents the computation of an optimal mean shape that can represent better the whole shape population. The anisotropic similarity transformation from the optimal mean shape to each individual training shape provides the ground truth of the pose parameters learned through the Marginal Space Learning (MSL). After the alignment with the estimated object pose, the optimal mean shape provides more accurate initialization than the mean shapes derived through a bounding box. Experiments on aortic valve landmark detection and whole-heart segmentation demonstrate the advantages of the approach.

Keywords

Aortic Valve Transcatheter Aortic Valve Implantation Similarity Transformation Transcatheter Aortic Valve Replacement Coronary Ostium 
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 Science+Business Media New York 2014

Authors and Affiliations

  • Yefeng Zheng
    • 1
  • Dorin Comaniciu
    • 1
  1. 1.Imaging and Computer VisionSiemens Corporate TechnologyPrincetonUSA

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