A method for automatically finding, without human intervention, a probability that a voxel of magnetic resonance imaging data of a subject corresponds to a given white matter fiber tract, comprising: obtaining a probabilistic fiber tract atlas for a nervous system, the probabilistic fiber tract atlas comprising: a plurality of atlas voxels that each represents a different volume element of the nervous system, the plurality of atlas voxels including a first atlas voxel that represents a first volume element of the nervous system and includes the given fiber tract, and probabilistic averaged information on locations and orientations of the given fiber tract in the first volume element of the nervous system, wherein the probabilistic averaged information on the orientations of the given fiber tract includes a rank-two symmetric atlas diffusion tensor having a first atlas diffusion tensor eigenvector ( v), such that v ' Tv is equal to the largest eigenvalue of T; acquiring magnetic resonance data, including diffusion data, from the nervous system of the subject, the magnetic resonance data comprising a plurality of data voxels; calculating magnetic resonance imaging diffusion data for the subject based on the acquired magnetic resonance data, the magnetic resonance imaging diffusion data including a calculated rank-two symmetric diffusion tensor for each of the data voxels; generating a diffusion vector ( v) for each of the data voxels, the diffusion vector being a first eigenvector corresponding to the largest eigenvalue of the calculated diffusion tensor for the corresponding data voxel; identifying a spatial correspondence between at least a first data voxel and the first atlas voxel by registering the acquired magnetic resonance data to the probabilistic fiber tract atlas; and using the probabilistic fiber tract atlas and the diffusion vector ( v) to find the probability P that the first data voxel represents the given fiber tract, wherein the probability P is defined by combining the atlas diffusion tensor, the first atlas diffusion tensor eigenvector ( v), and the diffusion vector ( v) of the first data voxel according to the equation v ′ T ̅ v v ̅ ′ T ̅ v ̅, wherein v' is the transpose of v and v ' is the transpose of v. a plurality of atlas voxels that each represents a different volume element of the nervous system, the plurality of atlas voxels including a first atlas voxel that represents a first volume element of the nervous system and includes the given fiber tract, and probabilistic averaged information on locations and orientations of the given fiber tract in the first volume element of the nervous system, wherein the probabilistic averaged information on the orientations of the given fiber tract includes a rank-two symmetric atlas diffusion tensor having a first atlas diffusion tensor eigenvector ( v), such that v ' Tv is equal to the largest eigenvalue of T; acquiring magnetic resonance data, including diffusion data, from the nervous system of the subject, the magnetic resonance data comprising a plurality of data voxels; calculating magnetic resonance imaging diffusion data for the subject based on the acquired magnetic resonance data, the magnetic resonance imaging diffusion data including a calculated rank-two symmetric diffusion tensor for each of the data voxels; generating a diffusion vector ( v) for each of the data voxels, the diffusion vector being a first eigenvector corresponding to the largest eigenvalue of the calculated diffusion tensor for the corresponding data voxel; identifying a spatial correspondence between at least a first data voxel and the first atlas voxel by registering the acquired magnetic resonance data to the probabilistic fiber tract atlas; and using the probabilistic fiber tract atlas and the diffusion vector ( v) to find the probability P that the first data voxel represents the given fiber tract, wherein the probability P is defined by combining the atlas diffusion tensor, the first atlas diffusion tensor eigenvector ( v), and the diffusion vector ( v) of the first data voxel according to the equation v ′ T ̅ v v ̅ ′ T ̅ v ̅, wherein v' is the transpose of v and v ' is the transpose of v.