REGISTRATION OF BRAIN RESECTION MRI WITH INTENSITY AND LOCATION PRIORS

Images with missing correspondences are difficult to align using standard registration methods due to the assumption that the same features appear in both images. To address this problem in brain resection images, we have recently proposed an algorithm in which the registration process is aided by an indicator map that is simultaneously estimated to distinguish between missing and valid tissue. We now extend our method to include both intensity and location information for the missing data. We introduce a prior on the indicator map using a Markov random field (MRF) framework to incorporate map smoothness and spatial knowledge of the missing correspondences. The parameters for the indicator map prior are automatically estimated along with the transformation and indicator map. The new method improves both segmentation and registration accuracy as demonstrated using synthetic and real patient data.