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UNSUPERVISED AUTOMATIC WHITE MATTER FIBER CLUSTERING USING A GAUSSIAN MIXTURE MODEL.

Meizhu Liu1, Baba C Vemuri, Rachid Deriche

  • 1Department of CISE, University of Florida, Gainesville, FL, 32611, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|January 4, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel framework for clustering white matter fiber bundles from diffusion tensor imaging. The method uses Gaussian mixture models and a robust divergence measure for accurate fiber analysis.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Fiber tracking in diffusion tensor images is crucial for clinical applications.
  • Accurate and efficient quantitative analysis of white matter tracts is in high demand.
  • Existing methods may lack robustness or efficiency in fiber clustering.

Purpose of the Study:

  • To propose a robust framework for fiber clustering.
  • To enable accurate and efficient quantitative analysis of white matter fiber bundles.
  • To automatically determine the number of clusters.

Main Methods:

  • Representing each fiber using a Gaussian mixture model (GMM).
  • Employing a statistically robust total square loss function for comparing GMMs.
  • Applying a hierarchical total Bregman soft clustering algorithm to GMMs.

Main Results:

  • Successfully clustered white matter fiber bundles using the proposed framework.
  • Demonstrated favorable performance on both synthetic and real diffusion tensor imaging data.
  • The method automatically determined the optimal number of clusters.

Conclusions:

  • The proposed framework offers a robust and efficient approach for fiber clustering.
  • Gaussian mixture models provide an effective fiber representation for quantitative analysis.
  • This method advances the capabilities for analyzing white matter microstructure.