Abstract

A new algorithm is presented for the automatic segmentation and classification of brain tissue from 3D MR scans. It uses discriminative Random Decision Forest classification and takes into account partial volume effects. This is combined with correction of intensities for the MR bias field, in conjunction with a learned model of spatial context, to achieve accurate voxel-wise classification. Our quantitative validation, carried out on existing labelled datasets, demonstrates improved results over the state of the art, especially for the cerebro-spinal fluid class which is the most difficult to label accurately