We present a novel methodology for the automated segmentation of Glioblastoma Multiforme tumors given only a high-resolution T1 post-contrast enhanced channel, which is routinely done in clinical MR acquisitions. The main contribution of the paper is the integration of contextual filter responses, to obtain a better class separation of abnormal and normal brain tissues, into the multilevel segmentation by weighted aggregation (SWA) algorithm. The SWA algorithm uses neighboring voxel intensities to form an affinity between the respective voxels. The affinities are then recursively computed for all the voxel pairs in the given image and a series of cuts are made to produce segments that contain voxels with similar intensity properties. SWA provides a fast method of partitioning the image, but does not produce segments with meaning. Thus, a contextual filter response component was integrated to label the aggregates as tumor or non-tumor. The contextual filter responses were computed via texture filter responses based on the gray level co-occurrence matrix (GLCM) method. The GLCM results in texture features that are used to quantify the visual appearance of the tumor versus normal tissue. Our results indicate the benefit of incorporating contextual features and applying non-linear classification methods to segment and classify the complex case of grade 4 tumors.