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Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
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A Bayesian framework for image segmentation with spatially varying mixtures.

Christophoros Nikou1, Aristidis C Likas, Nikolaos P Galatsanos

  • 1Department of Computer Science, University of Ioannina, Ioannina, Greece. cnikou@cs.uoi.gr

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|April 10, 2010
PubMed
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A novel Bayesian model enhances image segmentation using Gaussian mixture models (GMM) and spatial smoothness. This approach accurately segments images, outperforming existing GMM and state-of-the-art methods, even with noisy data.

Area of Science:

  • Computer Vision
  • Statistical Modeling
  • Machine Learning

Background:

  • Image segmentation is crucial for image analysis.
  • Existing Gaussian mixture models (GMM) often lack explicit spatial modeling.
  • Previous methods required computationally intensive or inexact solutions.

Purpose of the Study:

  • To introduce a new Bayesian model for image segmentation.
  • To incorporate spatial smoothness constraints within a GMM framework.
  • To develop an efficient and exact parameter estimation method.

Main Methods:

  • Utilizing a Dirichlet compound multinomial (DCM) distribution for mixing proportions.
  • Employing a Gauss-Markov random field (MRF) on Dirichlet parameters for smoothness.
  • Implementing a maximum a posteriori (MAP) expectation-maximization (EM) algorithm for closed-form updates.

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Main Results:

  • The proposed model explicitly models mixing proportions and enforces spatial smoothness.
  • Closed-form parameter updates are achieved via MAP-EM.
  • Numerical experiments show superior performance over other GMM-based and state-of-the-art methods.

Conclusions:

  • The new Bayesian GMM with DCM and MRF offers an effective and efficient image segmentation solution.
  • The model demonstrates robustness in segmenting natural and noisy images.
  • This approach advances GMM-based image segmentation techniques.