Three-dimensional partial volume segmentation of multispectral magnetic resonance images using stochastic relaxation

摘要

Abstract: An algorithm has been developed which uses stochastic relaxation in three dimensions to segment brain tissues from images acquired using multiple echo sequences from magnetic resonance imaging (MRI). The initial volume data is assumed to represent a locally dependent Markov random field. Partial volume estimates for each voxel are obtained yielding fractional composition of multiple tissue types for individual voxels. A minimum of user intervention is required to train the algorithm by requiring the manual outlining of regions of interest in a sample image from the volume. Segmentations obtained from multiple echo sequences are determined independently and then combined by forming the product of the probabilities for each tissues type. The implementation has been parallelized using a dataflow programming environment to reduce the computational burden. The algorithm has been used to segment 3D MRI data sets using multiple sclerosis lesions, gray matter, white matter, and cerebrospinal fluid as the partial volumes. Results correspond well with manual segmentations of the same data. !11