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The importance of accurate early diagnosis of dyslexia, which severely affects the learning abilities of children, cannot be overstated. Neuropathological studies have revealed an abnormal anatomy of the cerebral white matter (CWM) in dyslexic brains. We explore a possibility of distinguishing between dyslexic and normal (control) brains by a quantitative shape analysis of CWM gyrifications on 3D magnetic resonance (MR) images. Our approach consists of (i) segmentation of the CWM on a 3D brain image using a deformable 3D boundary; (ii) extraction of gyrifications from the segmented CWM, and (iii) shape analysis to quantify thickness of the extracted gyrifications and classify dyslexic and normal subjects. The boundary evolution is controlled by two probabilistic models of visual appearance of 3D CWM: the learned prior and the current appearance model. Initial experimental results suggest that the proposed 3D texture analysis is a promising supplement to the current techniques for diagnosing dyslexia.