Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms

  title={Partial-volume Bayesian classification of material mixtures in MR volume data using voxel histograms},
  author={David H. Laidlaw and Kurt W. Fleischer and Alan H. Barr},
  journal={IEEE Transactions on Medical Imaging},
The authors present a new algorithm for identifying the distribution of different material types in volumetric datasets such as those produced with magnetic resonance imaging (MRI) or computed tomography (CT). Because the authors allow for mixtures of materials and treat voxels as regions, their technique reduces errors that other classification techniques can create along boundaries between materials and is particularly useful for creating accurate geometric models and renderings from volume… CONTINUE READING
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From Laplace to supernova SN1987A: Bayesian inference in astrophysics

  • T. J. Loredo
  • inMaximum Entropy and Bayesian Methods , P…
  • 1989
Highly Influential
8 Excerpts

Goal-directed brain micro-imaging

  • David H. Laidlaw, Alan H. Barr, Russell E. Jacobs
  • inNeuroinformatics: An Overview of the Human…
  • 1997
1 Excerpt

Model Extraction from Magnetic Resonance Volume Data, Ph.D

  • David H. Laidlaw, Geometric
  • thesis, California Institute of Technology,
  • 1995
1 Excerpt

Mazoyer , “ Three - dimensional segmentation and interpolation of magnetic resonance brain images

  • Marc Joliot, M Bernard
  • IEEE Transactions on Medical Imaging
  • 1993

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