Viscous fluid model-based non-rigid registration incorporating scale space keypoints

Non-rigid registration of monomodal image often takes an important role in image-guided radiotherapy and surgery. Viscous fluid model is widely used to enforce the topological properties on the deformation, and thus constrain the enormous solution space. Intensity-based method is popular in non-rigid registration, but it is sensitive to intensity variations. In this paper, we develop a new algorithm integrating the intensity and feature of images. The local keypoint information extracted by scale invariant feature transform (SIFT) in scale space is incorporated into intensity similarity measures as the body force of viscous fluid model in a Bayesian framework. Experiments were performed on synthetic and clinical images of CT and MR. It is shown that the keypoint information significantly improves fluid model for non-rigid registration in accuracy.