Unsupervised segmentation of dual-echo MR images by a sequentially learned Gaussian mixture model

This paper proposes a method for unsupervised segmentation of brain tissues from dual-echo MR images without any prior knowledge about the number of tissues and their density distributions on each MRI echo. The brain tissues are described by a Finite Gaussian Mixture Model (FGMM). The FGMM parameters are learned by sequentially applying the Expectation Maximization (EM) algorithm to a stream of data sets which are specifically organized according to the global spatial relationship of the brain tissues. Preliminary results on actual MRI slices have shown the method to be promising.