EM based approximation of empirical distributions with linear combinations of discrete Gaussians

We propose novel Expectation Maximization (EM) based algorithms for accurate approximation of an empirical probability distribution of discrete scalar data. The algorithms refine our previous ones in that they approximate the empirical distribution with a linear combination of discrete Gaussians (LCDG). The use of the DGs results in closer approximation and considerably better convergence to a local likelihood maximum compared to previously involved conventional continuous Gaussian densities. Experiments in segmenting multi-modal medical images show the proposed algorithms produce more adequate region borders.