A unified Bayesian hierarchical model for MRI tissue classification

作者：Feng Dai^*; Liang Dong; Tierney Luke

来源：Statistics in Medicine, 2014, 33(8): 1349-1368.

DOI：10.1002/sim.6018

• parallel computation
• intensity non-uniformity
• neuroimaging
• partial volume effect
• normal mixture model
• Markov chain Monte Carlo
• conditional independence
• Sparse matrix
• repulsion Potts model
• Gaussian Markov random field model

Various works have used magnetic resonance imaging (MRI) tissue classification extensively to study a number of neurological and psychiatric disorders. Various noise characteristics and other artifacts make this classification a challenging task. Instead of splitting the procedure into different steps, we extend a previous work to develop a unified Bayesian hierarchical model, which addresses both the partial volume effect and intensity non-uniformity, the two major acquisition artifacts, simultaneously. We adopted a normal mixture model with the means and variances depending on the tissue types of voxels to model the observed intensity values. We modeled the relationship among the components of the index vector of tissue types by a hidden Markov model, which captures the spatial similarity of voxels. Furthermore, we addressed the partial volume effect by construction of a higher resolution image in which each voxel is divided into subvoxels. Finally, We achieved the bias field correction by using a Gaussian Markov random field model with a band precision matrix designed in light of image filtering. Sparse matrix methods and parallel computations based on conditional independence are exploited to improve the speed of the Markov chain Monte Carlo simulation. The unified model provides more accurate tissue classification results for both simulated and real data sets.

• 出版日期2014-4-15