This paper presents a novel method for the segmentation of anatomical structures in the white matter from DTI (Diffusion Tensor Imaging) data. Our approach is based on: (a) the use of a DTI white matter atlas to guide the segmentation process, (b) the use of tensor invariants and the orientation information of the tensor as features, and (c) a statistical modeling of the data with a level set implementation. This formulation allows for controlling the relative importance of the different properties of the diffusion tensor and uses the anatomical information of the atlas to constrain the segmentation. The method has been applied to the segmentation of DTI volumes, and results show it constitutes a valid alternative to other approaches such as VBM or TBSS for white matter analysis.