Probabilistic image registration and anomaly detection by nonlinear warping

Automatic, defect tolerant registration of transmission electron microscopy (TEM) images poses an important and challenging problem for biomedical image analysis, e.g. in computational neuroanatomy. In this paper we demonstrate a fully automatic stitching and distortion correction method for TEM images and propose a probabilistic approach for image registration. The technique identifies image defects due to sample preparation and image acquisition by outlier detection. A polynomial kernel expansion is used to estimate a non-linear image transformation based on intensities and spatial features. Corresponding points in the images are not determined beforehand, but they are estimated via an EM-algorithm during the registration process which is preferable in the case of (noisy) TEM images. Our registration model is successfully applied to two large image stacks of serial section TEM images acquired from brain tissue samples in a computational neuroanatomy project and shows significant improvement over existing image registration methods on these large datasets.