Accurate segmentation of the prostate and organs at risk is the fundamental guide for planning any radiotherapy. Such task is currently performed using a manual delineation of the organ on the MRI, a highly time consuming responsibility which in addition introduces inter and intra expert variability. Automatic MRI segmentation is a very challenging goal because of the large organ variability and the proximity of the neighboring organs. This work presents an automatic atlas-based segmentation strategy that selects the most probable template from a database using a robust multiscale similarity analysis. Once that probable template is selected, the associated segmentation is non-rigidly registered to the new MRI. The proposed method takes advantage of both the interindividual shape variation and intra-individual salient point representation. Results show that the method produces reliable segmentations, obtaining an average Dice Coefficient of 72% when comparing with the expert manual segmentation under a leave-one-out scheme with the training database.