In this paper we present HEAD, the Human Encephalon Automatic Delimiter, a new and efficient method for skull-stripping in T1-weighted MRI that combines an unique histogram analysis with binary mathematical morphology. In our experiments we use real images with highly variable noise ratios and intensity non-uniformity. We evaluate our results based on manually generated true masks and the well known Jaccard metric, achieving accuracy close to 99%. We compare our method with the popular Brain Extractor Surface algorithm (BSE), which in the same experiments achieved less than 95% of accuracy.