Brain atrophy measurement is increasingly important in studies of neurodegenerative diseases such as Alzheimer’s disease. From this perspective, a regional segmentation framework for magnetic resonance images has recently been developed by the team that I joined for my master thesis. It combines an atlas fusion and a tissue classification. A graph-cuts optimization step is then applied to obtain the final segmentation from the combination probability maps. To begin with neighboring constraints were integrated into the optimization step so as to prevent some labels to be adjacent in accordance with anatomical criteria. They were successfully tested on a restricted list of patient images which previously presented segmentation errors. Secondly, a multigrid tissue classification was implemented in order to compensate for the effects of intensity inhomogeneities. However, the visual observations on a few cases showed little improvement compared to the increased computation time. Consequently another possibility was investigated to modify the classification. An atlas-based classification was implemented and tested both on a small-scale and a large-scale. The efficiency of the proposed method was visually assessed on a few patients, especially regarding the separation between grey and white matter. The process was then applied on a database containing several hundreds patients and the results demonstrated an improved group separation based on grey matter volume, whose reduction is particularly significant with patients suffering from Alzheimer’s Disease. To conclude, several links of the segmentation framework have been upgraded, which promises good results for future regional atrophy studies.