We present and validate a novel diffusion tensor imaging (DTI) approach for segmenting the human whole-brain into partitions representing grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF). The approach utilizes the contrast among tissue types in the DTI anisotropy vs. diffusivity rotational invariant space. The DTI-based whole-brain GM and WM fractions (GMf and WMf) are contrasted with the fractions obtained from conventional magnetic resonance imaging (cMRI) tissue segmentation (or clustering) methods that utilized dual echo (proton density-weighted (PDw)), and spin-spin relaxation-weighted (T2w) contrast, in addition to spin-lattice relaxation weighted (T1w) contrasts acquired in the same imaging session and covering the same volume. In addition to good correspondence with cMRI estimates of brain volume, the DTI-based segmentation approach accurately depicts expected age vs. WM and GM volume-to-total intracranial brain volume percentage trends on the rapidly developing brains of a cohort of 29 children (6-18 years). This approach promises to extend DTI utility to both micro and macrostructural aspects of tissue organization.