MRI tissue classification with neighborhood statistics: a nonparametric, entropy-minimizing approach

Med Image Comput Comput Assist Interv. 2005;8(Pt 2):517-25. doi: 10.1007/11566489_64.

Abstract

We introduce a novel approach for magnetic resonance image (MRI) brain tissue classification by learning image neighborhood statistics from noisy input data using nonparametric density estimation. The method models images as random fields and relies on minimizing an entropy-based metric defined on high dimensional probability density functions. Combined with an atlas-based initialization, it is completely automatic. Experiments on real and simulated data demonstrate the advantages of the method in comparison to other approaches.

Publication types

  • Comparative Study
  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms*
  • Artifacts*
  • Artificial Intelligence*
  • Computer Simulation
  • Entropy
  • Humans
  • Image Enhancement / methods*
  • Image Interpretation, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods
  • Information Storage and Retrieval / methods
  • Magnetic Resonance Imaging / methods*
  • Models, Biological
  • Models, Statistical
  • Numerical Analysis, Computer-Assisted
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Signal Processing, Computer-Assisted
  • Subtraction Technique