Work

Markov random field segmentation of brain MR images

Year: 1997

Type: article

Abstract: We describe a fully-automatic three-dimensional (3-D)-segmentation technique for brain magnetic resonance (MR) images. By means of Markov random fields (MRF's) the segmentation algorithm captures three features that are of special importance for MR images, i.e., nonparametric distributions of tissue intensities, neighborhood correlations, and signal inhomogeneities. Detailed simulations and real MR images demonstrate the performance of the segmentation algorithm. In particular, the impact of noise, inhomogeneity, smoothing, and structure thickness are analyzed quantitatively. Even single-echo MR images are well classified into gray matter, white matter, cerebrospinal fluid, scalp-bone, and background. A simulated annealing and an iterated conditional modes implementation are presented.

Source: IEEE Transactions on Medical Imaging

DOI: 10.1109/42.650883

Authors: K. Held, E.R. Kops, B.J. Krause, W.M. Wells, R. Kikinis, H.-W. Muller-Gartner