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How to Measure Cortical Folding from MR Images: a Step-by-Step Tutorial to Compute Local Gyrification Index
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Cortical Folding Development Study based on Over-Complete Spherical Wavelets.

Peng Yu1, Boon Thye Thomas Yeo2, P Ellen Grant3

  • 1Health Sciences and Technology, MIT, Cambridge, MA, 02139.

Proceedings. IEEE International Conference on Computer Vision
|June 18, 2015
PubMed
Summary
This summary is machine-generated.

Over-complete spherical wavelets offer improved shape analysis for 2D surfaces, overcoming aliasing issues found in bi-orthogonal wavelets. This advancement enables more stable cortical folding models and better detection of developmental regions in newborns.

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Area of Science:

  • Medical image analysis
  • Computational geometry
  • Applied mathematics

Background:

  • Bi-orthogonal spherical wavelets are effective for 2D closed surface segmentation and analysis.
  • Existing bi-orthogonal methods face aliasing issues, limiting rotational invariance.

Purpose of the Study:

  • Introduce over-complete spherical wavelets for enhanced 2D surface shape analysis.
  • Demonstrate the theoretical and practical advantages over bi-orthogonal wavelets.
  • Apply the new method to cortical folding development modeling.

Main Methods:

  • Development and application of over-complete spherical wavelet transforms.
  • Analysis of synthetic and real-world 2D surface data.
  • Modeling of cortical folding development using the novel wavelet approach.

Main Results:

  • Over-complete spherical wavelets provide theoretical advantages over bi-orthogonal methods.
  • The technique demonstrates utility on both synthetic and real datasets.
  • Improved stability in cortical folding development models was achieved.
  • A wider range of folding development regions were detected in a newborn dataset.

Conclusions:

  • Over-complete spherical wavelets represent a significant advancement for 2D surface shape analysis.
  • This method enhances the stability and detection capabilities for neurodevelopmental studies.
  • The approach offers improved rotational invariance and reduced aliasing artifacts.