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A new algorithm for segmenting a multi-modal grey-scale image is proposed. The image is described as a sample of a joint Gibbs random field of region labels and grey values. To initialize the model, a multi-modal mixed empirical grey level density distribution is approximated with several linear combinations of Gaussians, one linear combination per region. Bayesian decisions involving Expectation-Maximization and genetic optimization techniques are used to sequentially estimate and refine parameters of the model, including the number of Gaussians for each region. The final estimates are more accurate than with conventional normal mixture models and result in more adequate region borders in the image. Experiments with simulated and real medical CT images confirm the accuracy of our approach.

Precise Image Segmentation by Iterative EM-Based Approximation of Empirical Grey Level Distributions with Linear Combinations of Gaussians