Expectation-maximization for a linear combination of Gaussians
Georgy Gimel’farb, A.A. Farag, Ayman S El-Baz · Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004. · 2004
We propose a modified expectation-maximization algorithm that approximates an empirical probability density function of scalar data with a linear combination of Gaussians (LCG). Due to both positive and negative components, the LCG approximates inter-class transitions more accurately than a conventional mixture of only positive Gaussians. Experiments in segmenting multi-modal medical images show the proposed LCG-approximation results in more adequate region borders.