Segmentation of MR images with intensity inhomogeneities

A statistical model to segment clinical magnetic resonance (MR) images in the presence of noise and intensity inhomogeneities is proposed. Inhomogeneities are considered to be multiplicative low-frequency variations of intensities that are due to the anomalies of the magnetic fields of the scanners. The measurements are modeled as a Gaussian mixture where inhomogeneities present a bias field in the distributions. The piecewise contiguous nature of the segmentation is modeled by a Markov random field (MRF). A greedy algorithm based on the iterative conditional modes (ICM) algorithm is used to find an optimal segmentation while estimating the model parameters. Results with simulated and hand-segmented images are presented to compare performance of the algorithm with other statistical methods. Segmentation results with MR head scans acquired from four different clinical scanners are presented. (C) 1998 Elsevier Science B.V.