A computer-implemented method of mapping a target tissue of a subject, the method comprising: providing diffusion tensor imaging (DTI) scan data of a plurality of voxels in one or more slabs of a target tissue of a subject; calculating a respective diffusion tensor for each voxel in the plurality of voxels in the target tissue; determining, for each voxel, eigenvalues and eigenvectors of the respective diffusion tensor and a respective set of diffusion parameters, wherein the respective set of diffusion parameters associated with each voxel comprises (i) a diffusion parameter M , which is a substantially monotonically increasing function of each of the eigenvalues of the respective diffusion tensor and which is indicative of a magnitude of the diffusion in the voxel, and (ii) an additional diffusion parameter indicative of an anisotropy or isotropy of the diffusion in the voxel; partitioning the voxels into two groups, a first group and a second group, wherein the voxels, whose respective set of diffusion parameters is such that each diffusion parameter is less than a corresponding element in a set of thresholds, are in the first group; and generating a graphical representation of a diffusion ellipsoid map made from diffusion ellipsoids associated with the first group and the second group of voxels of at least one of the one or more slabs of the target tissue, wherein the diffusion ellipsoids associated with voxels in the first group are colored differently to the diffusion ellipsoids associated with the second group; wherein voxels, whose respective set of diffusion parameters is such that at least one of the diffusion parameters is greater than, or equal to, a corresponding element in the set of thresholds are in the second group; and wherein diffusion ellipsoid maps, including adjacent diffusion ellipsoids belonging to the first group, are indicative of cancer in the target tissue. providing diffusion tensor imaging (DTI) scan data of a plurality of voxels in one or more slabs of a target tissue of a subject; calculating a respective diffusion tensor for each voxel in the plurality of voxels in the target tissue; determining, for each voxel, eigenvalues and eigenvectors of the respective diffusion tensor and a respective set of diffusion parameters, wherein the respective set of diffusion parameters associated with each voxel comprises (i) a diffusion parameter M , which is a substantially monotonically increasing function of each of the eigenvalues of the respective diffusion tensor and which is indicative of a magnitude of the diffusion in the voxel, and (ii) an additional diffusion parameter indicative of an anisotropy or isotropy of the diffusion in the voxel; partitioning the voxels into two groups, a first group and a second group, wherein the voxels, whose respective set of diffusion parameters is such that each diffusion parameter is less than a corresponding element in a set of thresholds, are in the first group; and generating a graphical representation of a diffusion ellipsoid map made from diffusion ellipsoids associated with the first group and the second group of voxels of at least one of the one or more slabs of the target tissue, wherein the diffusion ellipsoids associated with voxels in the first group are colored differently to the diffusion ellipsoids associated with the second group; wherein voxels, whose respective set of diffusion parameters is such that at least one of the diffusion parameters is greater than, or equal to, a corresponding element in the set of thresholds are in the second group; and wherein diffusion ellipsoid maps, including adjacent diffusion ellipsoids belonging to the first group, are indicative of cancer in the target tissue. <EOS>