Diffusion tensor MRI (DT-MRI) allows the in vivo assessment of the abnormalities of white matter in multiple sclerosis (MS). DT-MRI is complementary to conventional MRI sequences where such abnormalities are often not visible. Most studies have shown differences of mean diffusivity (MD) and fractional anisotropy (FA) between patients and controls in MS lesions (MSL) and normal appearing white matter (NAWM) based on histogram analyses. However, the majority of these studies are based on histogram analysis, i.e. local information of DT-MRI is lost, and moreover a number of those studies were not conclusive, partly explained by methodological issues, because these tensor indices vary within the brain, which is likely to make such global, histogram-based analyses, fail. Here we propose a new framework to compare these indices between MSL and NAWM and between two populations (patients and controls). First, MSL are manually delineated in MS patients. The mid-sagittal plane is then automatically computed, allowing to define a contralateral region of interest (ROI) in NAWM for each MSL. This allows the local comparison of DTI indices in anatomically similar regions in each MS patient. Second, each MS patient is linearly registered to each control subject, and the same left-right comparison between MSL and contralateral NAWM is then performed in controls. The results (ANOVA with multiple comparisons procedure) show that 1) FA values are lower in MSL than in contralateral NAWM in MS patients (p < 0.05) but not in controls, 2) FA values are lower in MS patients (MSL and contralateral NAWM) compared to controls (p < 0.05), 3) MD values are not different between MSL/contralateral NAWM in MS patients and controls. We also show that combining different preprocessing methods (3 estimation methods and 3 distortion correction methods) has little impact on such results. Nevertheless, our fully automated approach is superior to manual or semi-automated DT-MRI analyses regarding the robustness of the results (reproducibility and accuracy).