Robust partial-volume tissue classification of cerebral MRI scans

In magnetic resonance images (MRI) a voxel may contain multiple tissue types (partial volume effect). We concentrate in the classification of these voxels using an adaptive Bayesian approach and pay particular attention to practical implementation problems induced by the modeling of partial volume voxels. Moreover, we show that this algorithm is suitable to perform tissue classification of brain MRI scans that in turn can be used for visualization or quantitative analysis, or for further purposes such as brain image registration. Results are presented showing the efficacy of the method as compared to a binary classification process.