PURPOSE: Image texture has recently attracted much attention in providing quantitative features that are unique to various different tissue types, in particular, in MR images of the brain. Such image features may be useful for tumor response quantification. As a first step, one needs to establish if these features are sensitive to different tissues of clinical relevance. Here, a novel method of texture analysis based on the Hartley transform has been investigated and applied to MR images of glioblastoma multiforme (GBM). METHODS: Contrast-enhanced T1-weighted gradient-echo and T2-FLAIR spin-echo MR images of 27 GBM patients acquired prior to radiation therapy were available for analysis. Before computing texture features on these images, a novel image transformation was employed in the form of a power map computed from the localized Hartley transform of the image. Haralick statistical texture features were then computed based on the power map. This method was compared to the standard approach of obtaining texture features directly from the image. Twelve different features were computed on different resolution levels. On a regional resolution level, image texture features were identified that are able to correctly classify entire regions within T1-weighted and T2-FLAIR brain MR images of GBM patients into abnormal (containing contrast-enhancing GBM tumor) and brain tissue. Various metrics [area under the ROC curve (AUC), maximum accuracy, and Canberra distance] have been computed to quantify the usefulness of these features. On a local resolution level, it was investigated which of these features are able to provide a voxel-by-voxel enhancement that could be used for assisting the segmentation of the gross tumor volume on T1 images. The "gold standard" for this analysis was a gross tumor volume corresponding to the contrast-enhancing lesion visualized on T1-weighted images as segmented by a radiation oncologist. RESULTS: The Sum-mean and Variance features demonstrated the best performance overall. For the T1-weighted images, the identification performance of Sum-mean and Variance features computed from the power map was higher (AUC = 0.9959 and AUC = 0.9918, respectively) and with higher Canberra distances as compared to features computed directly from the images (AUC = 0.8930 and AUC = 0.9163, respectively). These results in T2-FLAIR images were even superior. The features computed from the power map showed an unequivocal identification (AUC = 1) with higher Canberra distances, whereas the performance of the features from the original images was slightly lower (AUC = 0.9739 and AUC = 0.9904, respectively). The same features computed on the power map of the T1-weighted images also provided superior enhancement in individual tumor voxels as compared to the features computed on the original images. CONCLUSIONS: The Sum-mean and Variance features are both useful for identifying and segmenting GBM tumors on localized Hartley transformed MR images.