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A curve evolution approach to object-based tomographic reconstruction.

Haihua Feng1, William Clem Karl, David A Castañon

  • 1MathWorks Inc., Natick, MA 01760, USA. hfeng@mathworks.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
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This study introduces a novel geometric curve evolution method for tomographic reconstruction, improving object shape accuracy and offering computational benefits over traditional pixel-based techniques.

Area of Science:

  • Image reconstruction
  • Computational imaging
  • Geometric methods

Background:

  • Tomographic reconstruction traditionally relies on pixel-based methods.
  • Existing techniques can be computationally intensive and may struggle with complex object shapes.

Purpose of the Study:

  • To develop a new tomographic reconstruction approach using geometric curve evolution.
  • To represent objects using texture coefficients and contours for improved reconstruction.

Main Methods:

  • Utilizing geometric curve evolution techniques and a novel "tomographic flow".
  • Employing level set methods for efficient algorithm development.
  • Jointly estimating object/background contours and texture coefficients.

Main Results:

Related Experiment Videos

  • Demonstrated performance on noisy limited-view Radon transform and ground-penetrating radar data.
  • Achieved improved shape reconstruction compared to conventional methods.
  • Showcased potential computational and memory advantages.

Conclusions:

  • Curve evolution methods offer a promising alternative for tomographic reconstruction.
  • The approach enhances accuracy in shape representation.
  • Potential for more efficient and memory-saving tomographic inversion.