Adaptive partial volume classification of MRI data

Tomographic biomedical images are commonly affected by an imaging artefact known as the partial volume (PV) effect. The PV effect produces voxels composed of a mixture of tissues in anatomical magnetic resonance imaging (MRI) data resulting in a continuity of these tissue classes. Anatomical MRI data typically consist of a number of contiguous regions of tissues or even contiguous regions of PV voxels. Furthermore discontinuities exist between the boundaries of these contiguous image regions. The work presented here probabilistically models the PV effect using spatial regularization in the form of continuous Markov random fields (MRFs) to classify anatomical MRI brain data, simulated and real. A unique approach is used to adaptively control the amount of spatial regularization imposed by the MRF. Spatially derived image gradient magnitude is used to identify the discontinuities between image regions of contiguous tissue voxels and PV voxels, imposing variable amounts of regularization determined by simulation. Markov chain Monte Carlo (MCMC) is used to simulate the posterior distribution of the probabilistic image model. Promising quantitative results are presented for PV classification of simulated and real MRI data of the human brain.