Continuous Gaussian mixture modeling

  • Stephen Aylward
  • Stephen Pizer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1230)


When the projection of a collection of samples onto a subset of basis feature vectors has a Gaussian distribution, those samples have a generalized projective Gaussian distribution (GPGD). GPGDs arise in a variety of medical images as well as some speech recognition problems. We will demonstrate that GPGDs are better represented by continuous Gaussian mixture models (CGMMs) than finite Gaussian mixture models (FGMMs).

This paper introduces a novel technique for the automated specification of CGMMs, height ridges of goodness-of-fit. For GPGDs, Monte Carlo simulations and ROC analysis demonstrate that classifiers utilizing CGMMs defined via goodness-of-fit height ridges provide consistent labelings and compared to FGMMs provide better true-positive rates (TPRs) at low false-positive rates (FPRs). The CGMM-based classification of gray and white matter in an inhomogeneous magnetic resonance (MR) image of the brain is demonstrated.


Gaussian Mixture Model Intensity Inhomogeneity Gradient Ascent Height Ridge Maximum Likelihood Expectation Maximization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Stephen Aylward
    • 1
  • Stephen Pizer
    • 2
  1. 1.Department of RadiologyUniversity of North CarolinaChapel Hill
  2. 2.Department of Computer Science Medical Image Display and Analysis GroupUniversity of North CarolinaChapel Hill

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