Validation of brain segmentation and tissue classification algorithm for T1-weighted MR images

Volumetric analysis of the brain from MR images is an important biomedical research tool. Segmentation of the brain parenchyma and its constituent tissue types, the gray matter and the white matter, is necessary for volumetric information in longitudinal and cross-sectional studies. We have implemented and compared two different classes of algorithms for segmentation of the brain parenchyma. In the first algorithm a combination of automatic thresholding and 3-D mathematical morphology was used to segment the brain while in the second algorithm an optical flow-based 3-D non-rigid registration approach was used to warp an MR head atlas to the subject brain. For tissue classification within the brain area a 3-D Markov Random Field model was used in conjunction with supervised and unsupervised classification. The algorithms described above were validated on a data set provided at the Internet Brain Segmentation Repository that consists of 20 normal T1 volumes (3 nim. slice thickness) with manually segmented brain and manually classified tissues. While the morphological segmentation algorithm had an average similarity index of 0.918, the atlas-based brain segmentation algorithm has an average similarity index of 0.953. The supervised tissue classification had an average similarity index of 0.833 for gray matter voxels and 0.766 for white matter voxels. The performance of these algorithms is quite acceptable to end-users both in terms of accuracy and speed.