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Sparse Deformable Models with Application to Cardiac Motion Analysis

  • Yang Yu
  • Shaoting Zhang
  • Junzhou Huang
  • Dimitris Metaxas
  • Leon Axel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)

Abstract

Deformable models have been widely used with success in medical image analysis. They combine bottom-up information derived from image appearance cues, with top-down shape-based constraints within a physics-based formulation. However, in many real world problems the observations extracted from the image data often contain gross errors, which adversely affect the deformation accuracy. To alleviate this issue, we introduce a new family of deformable models that are inspired from compressed sensing, a technique for efficiently reconstructing a signal based on its sparseness in some domain. In this problem, we employ sparsity to represent the outliers or gross errors, and combine it seamlessly with deformable models. The proposed new formulation is applied to the analysis of cardiac motion, using tagged magnetic resonance imaging (tMRI), where the automated tagging line tracking results are very noisy due to the poor image quality. Our new deformable models track the heart motion robustly, and the resulting strains are consistent with those calculated from manual labels.

Keywords

Control Point Internal Force Deformable Model Active Contour Model Medical Image Analysis 
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 2013

Authors and Affiliations

  • Yang Yu
    • 1
  • Shaoting Zhang
    • 1
  • Junzhou Huang
    • 2
  • Dimitris Metaxas
    • 1
  • Leon Axel
    • 3
  1. 1.Department of Computer ScienceRutgers UniversityPiscatawayUSA
  2. 2.Computer Science and EngineeringUniversity of Texas at ArlingtonUSA
  3. 3.Radiology DepartmentNew York UniversityNew YorkUSA

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