Abstract
A common approach for non-rigid medical image registration is the hierarchical image subdivision-based strategy. In this approach, images are progressively subdivided, locally registered, and elastically interpolated. Although this approach seems to be among the fastest approaches for non-rigid registration, computation time is still a real challenge. This work deals with this problem and proposes a new hierarchical strategy. To reduce Computational complexity, we propose to combine in the same framework the hierarchical image subdivision-based strategy with a Gaussian pyramid. The hierarchical subdivision method ensures that the registration process deals with small and large deformations, whereas the use of Gaussian pyramid decreases the computation time enormously. The proposed framework is preliminary validated in the context of monomodal registration by matching breast mammograms and MRI brain images with Simulated deformations. Registration quality is evaluated by using image differences, mean square error, peak signal to noise ratio and correlation coefficient. Complexity study and experimental results show that the proposed approach reduces considerably the computation cost meanwhile maintaining comparable accuracy.