Hypothesis testing with nonlinear shape models

Inf Process Med Imaging. 2005;19:15-26. doi: 10.1007/11505730_2.

Abstract

We present a method for two-sample hypothesis testing for statistical shape analysis using nonlinear shape models. Our approach uses a true multivariate permutation test that is invariant to the scale of different model parameters and that explicitly accounts for the dependencies between variables. We apply our method to m-rep models of the lateral ventricles to examine the amount of shape variability in twins with different degrees of genetic similarity.

MeSH terms

  • Algorithms*
  • Artificial Intelligence*
  • Cerebral Ventricles / pathology*
  • Computer Simulation
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Models, Biological
  • Models, Statistical
  • Nonlinear Dynamics
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Schizophrenia / pathology*
  • Sensitivity and Specificity
  • Twins, Monozygotic