Gabor wavelet analysis is used to extract the texture features of magnetic resonance (MR) tumor images to differentiate between primary central nervous system lymphoma (PCNSL) and glioblastoma multiforme (GBM). Gabor wavelet transform with eight orientations and various frequencies is performed on contrast-enhanced T1-weighted MR images to extract the discriminant features, including tumor shape information. A classification model is built based on the extracted features. Experiments show that the proposed hybrid method, which uses wavelet analysis, Gabor wavelet analysis, a support vector machine classifier, and linear discriminant analysis, can distinguish different diagnosis categories of tumor images.