Low-Complexity Nonrigid Image Registration Using Feature-Based Diffeomorphic Log-Demons

Traditional hybrid-type nonrigid registration algorithm uses affine transformation or class-specific distortion parameters for global matching assuming linear-type deformations in images. In order to consider generalized and nonlinear-type deformations, this paper presents an approach of feature-based global matching algorithm prior to certain local matching. In particular, the control points in images are identified globally by the well-known robust features such as the SIFT, SURF, or ASIFT and interpolation is carried out by a low-complexity orthogonal polynomial transformation. The local matching is performed using the diffeomorphic Demons, which is a well-established intensity-based registration method. Experiments are carried out on synthetic distortions such as spherical, barrel, and pin-cushion in commonly referred images as well as on real-life distortions in medical images. Results reveal that proposed introduction of feature-based global matching significantly improves registration performance in terms of residual errors, computational complexity, and visual quality as compared to the existing methods including the log-Demons itself.