We propose an algorithm for automated segmentation of white matter in brain MRI images, which can be used to create connected representations of the gray matter in the cerebral cortex of the brain. These representations then provide meaningful visualizations of brain activity data obtained from fMRI studies. Our algorithm to segment the white matter from the rest of the image is based on an active-contour scheme - STACS, and thus inherits all the advantages active-contour schemes possess. The segmentation, performed in three different planes of image capture, is driven by the statistics of the image. We combine the segmentation results from the three planes by a majority voting procedure to classify each voxel in the image as white matter or not. We improve the runtime of the algorithm by rewriting the force computation as a multiscale transformation. Initial results of labeling the white matter with an accuracy of about 89% show great promise of the proposed algorithm.