No standalone method is yet reported to differentiate PCNSL and grade-IV glioma with highest accuracy and diagnostic confidence. Imaging based differentiation between these two types has been a challenging problem. Objective of this study is to evaluate performance of texture-based features of SWI in improved differentiation of PCNSL from grade-IV gliomas. Proposed approach based upon texture feature-extraction from segmented SWI lesion, enabled automatic classification of tumors into primary central nervous system lymphoma and grade-IV glioma cases. One of the texture feature, Contrast, provided highest AUC along with high sensitivity and specificity. This classification might improve diagnosis and grading of tumors.