The preprocessing of 3-dimensional (3D) MRI data constitutes a bottleneck in the process of visualizing the brain surface with volume rendering. As a fast way to achieve this preprocessing, the authors propose a simple pipeline based on an algorithm of seedgrowing type, for approximate segmentation of the intradural space in T1-weighted 3D MRI data. Except for the setting of a seed and four parameters, this pipeline proceeds in an unsupervised manner; no interactive intermediate step is involved. It was tested with 15 datasets from normal adults. The result was reproducible in that as long as the seed was located within the cerebral white matter, identical segmentation was achieved for each dataset. Although the pipeline ran with gross segmentation error along the floor of the cranial cavity, it performed well along the cranial vault so that subsequent volume rendering permitted the observation of the sulco-gyral pattern over cerebral convexities. Use of this pipeline followed by volume rendering is a handy approach to the visualization of the brain surface from 3D MRI data.