We present an automatic segmentation method using the Maximum a posterior (MAP)-Markov random field (MRF) framework that possesses regional adaptive capability for the segmentation of MR brain images with the presence of noise and the intensity inhomogeneity. A spatial-varying Gaussian mixture (SVGM) is used to model the conditional probability distribution of each of the three brain tissues (WM, GM and CSF), and the MRF is used to represent prior probabilities. A three-component random vector consisting of spatial and intensity information is used in SVGM such that the model can represent more complex image characteristics, such as intensity inhomogeneity. The regional adaptive capability is achieved by imposing a similarity criterion that minimizes the intensity variation of the segmented tissues in local regions. Initial parameters of SVGM are estimated from a brain atlas using either the expectation maximization (EM) algorithm or modified k-mean (MKM) algorithm, and the iterated conditional modes (ICM) algorithm is used to perform the segmentation. The parameters estimation and the ICM algorithm are alternatively operated until a stopping criterion is reached. The implement of the method is validated by quantitatively comparing with other published classification methods on fifteen simulated and twenty in vivo brain volumes using the same evaluation criteria. Our algorithm demonstrates superior performance in all cases other than two, and is especially suitable for the volumes with higher noise and inhomogeneity. The complete experiments illustrate that the incorporations of the spatial information and the regional adaptive processing into MAP-MRF framework provide better solution to the segmentation of MR brain images.
['Zhigang Peng', 'William G. Wee', 'Jing-Huei Lee']
Automatic Segmentation of MR Brain Images Using Spatial-Varying Gaussian Mixture and Markov Random Field Approach