Unsupervised segmentation of three-dimensional brain images
This paper presents an unsupervised segmentation method applied to classify brain tissues in 3D for magnetic resonance (MR) images. An MR image volume may be composed of a mixture of several tissue types due to partial volume effects. The statistical model of the mixtures is proposed and studied by means of simulations. It is shown that it can be approximated by a Gaussian function under some conditions. The D'Agostino-Pearson normality test is used to calculate the risk /spl alpha/ of the approximation. In order to classify a brain into three brain tissues and deal with the problem of partial volume effects, the proposed algorithm classifies firstly the brain into pure classes and mix-classes, it then re-classifies the mix-classes into pure classes by adding the knowledge about the topology of the brain, based on the multifractal dimension. Both steps use Markov random field models. The algorithm is evaluated using both simulated images and real MR images.