Registration of images with missing correspondences, such as in the alignment of preoperative and postresection brain data, is a difficult task. To simplify this problem, we introduce an indicator map to segment valid correspondence regions from areas with missing data. The registration problem is posed in a marginalized maximum a posteriori (MAP) estimation framework in which the transformation and correspondence regions are simultaneously estimated using the expectation-maximization (EM) algorithm. The E-step calculates the weights of the possible indicator maps while the M-step updates the transformation. A spatial prior based on principal component analysis (PCA) is used to guide indicator map selection. We demonstrate the promise of our approach on synthetic and real data by comparing results using our algorithm to those from a standard non-rigid registration method.