It is difficult to completely differentiate patients with relapsing neuromyelitis optica (RNMO) from relapsing-remitting multiple sclerosis (RRMS) for their similarities in clinical manifestation. In this study, we proposed a novel approach, using two-dimensional histogram of apparent diffusion coefficient (ADC) and fractional anisotropy (FA) of the brain derived from diffusion tensor imaging (DTI) as classification feature, to discriminate patients with RNMO from RRMS. In this approach, two-dimensional principal component analysis (2D-PCA) was used to extract feature and reduce dimensionality of matrix-formed data efficiently. Then linear discriminant analysis (LDA) was performed on these extracted features to find the best projection direction to separate patients with RNMO from RRMS. Finally, a minimum distance classifier was generated on the basis of projection scores. The correct recognition rate of our method reached 85.7%, validated by the leave-one-out method. This result was much higher than that using feature of ADC or FA separately (59.5% for ADC, 76.2% for FA). In conclusion, the proposed method on the basis of combined features is more effective for classification than those merely using the features separately, and it may be helpful in differentiating RNMO from RRMS patients. (c) 2006 Elsevier Inc. All rights reserved.