In this paper, we propose a new adaptive atlas-based technique for the automated segmentation of brain tissues (white matter and grey matter) from infant diffusion tensor images (DTI). Brain images and desired region maps (brain, Cerebrospinal fluid, etc.) are modeled by a joint Markov-Gibbs random field (MGRF) model of independent image signals and interdependent region labels. The proposed joint MGRF model accounts for the following three descriptors: (i) a 1st-order visual appearance to describe the empirical distribution of six features that has been estimated from the DTI in addition to the non-diffusion (b0) scans, (ii) 3D probabilistic atlases, and (iii) a 3D spatially invariant 2nd-order homogeneity descriptor. The 1st-order visual appearance descriptor, assuming each of the estimated DTI parameters are independent, is precisely approximated using our previously developed linear combination of discrete Gaussians (LCDG) intensity model that includes positive and negative Gaussian components. The 3D probabilistic atlases are learned using a subset of the 3D co-aligned training DTI brain images. The 2nd-order homogeneity descriptor is modeled by a 2nd-order translation and rotation invariant MGRF of region labels, with analytically estimated potentials. We tested our approach on 25 DTI brain images, and evaluated the performance on 5 manually segmented 3D DTI brain images to confirm the high accuracy of the proposed approach, as evidenced by the Dice similarity, Hausdorff distance, and absolute volume difference metrics.