Near-Tubular Fiber Bundle Segmentation for Diffusion Weighted Imaging: Segmentation Through Frame Reorientation

Abstract

This paper proposes a methodology to segment near-tubular fiber bundles from diffusion weighted magnetic resonance images (DW-MRI). Segmentation is simplified by locally reorienting diffusion information based on large-scale fiber bundle geometry. Segmentation is achieved through simple global statistical modeling of diffusion orientation. Utilizing a modification of a recent segmentation approach by Bresson et al. allows for a convex optimization formulation of the segmentation problem, combining orientation statistics and spatial regularization. The approach compares favorably with segmentation by full-brain streamline tractography.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 2010
Accession Number
AD1021299

Entities

People

  • Allen Tannenbaum
  • Christopher Zach
  • John Melonakos
  • Marc Niethammer

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • Biomedical Research
  • Brain
  • Computational Science
  • Computations
  • Computer Science
  • Computer Vision
  • Coordinate Systems
  • Diagnostic Imaging
  • Geometry
  • Image Processing
  • Information Processing
  • Magnetic Resonance
  • Pattern Recognition
  • Probability
  • Probability Distributions
  • Statistics

Readers

  • Materials Science and Engineering.
  • Medical Imaging.
  • Neural Network Machine Learning.