[en] The classification of each point in space to a given label for a given data set is achieved by finding the segmentation that maximizes the probability of input given the prior probabilities from the training set. First, the probability of a class at each point is computed as the probability that the given class appeared at that location in the training set times the likelihood of getting the subject-specific measured value from that class. The latter is computed from the PDF for that label as estimated from the training set. The probability of each class at each point is computed. An initial segmentation is generated by assigning each point to the class for which the probability is greatest. Given this segmentation, the neighborhood function is used to recompute the class probabilities. The data set is resegmented based on this new set of class probabilities. This is repeated until the segmentation does not change. This procedure allows the atlas to be customized for each data set by using the information specific to that data set. Once complete, not only do we have a label for each point in space, but we also have the probability of seeing the measured value at each voxel. The product of this probability over all points in space yields the probability of the input. This will be used later during automatic failure detection. This procedure has been shown to be statistically indistinguishable from manual rates (Fischl, et al, 2002) and relatively insensitive to changes in acquisition parameters (Fischl, et al, 2004a).