The detection of brain alterations is crucial for understanding pathophysiological or neurodegenerative processes. The Voxel-Based Morphometry (VBM) is one of the most popular methods to achieve this task. Based on the comparison of local averages of tissue densities, VBM has been used in a large number of studies. Despite its numerous advantages , VBM is based on a highly reduced representation of the local brain anatomy since complex anatomical patterns are reduced to local averages of tissue probabilities. In this paper, we propose a new framework called Sparse-Based Morphometry (SBM) to better represent local brain anatomies. The presented patch-based approach uses dictionary learning to detect anatomical pattern modifications based on their shape and geometry. In our validation, the impact of the patch and group sizes is evaluated. Moreover, the sensitivity of SBM along Alzheimer's Disease (AD) progression is compared to VBM. Our results indicate that SBM is more sensitive than VBM on small groups and to detect early anatomical modifications caused by AD.