This paper presents a new feature matching algorithm for nonrigid multimodal image registration. The proposed algorithm first constructs phase congruency representations (PCR) of images to be registered. Then scale invariant feature transform (SIFT) method is applied to capture significant feature points from PCR. Subsequently, the putative matching is obtained by the nearest neighbour matching in the SIFT descriptor space. The SIFT descriptor is then integrated into Coherent Point Drift (CPD) method so that the appropriate matching of two point sets is solved by combining appearance with distance properties between putative match candidates. Finally, the transformation estimated by matching the point sets is applied to registration of original images. The results show that the proposed algorithm increases the correct rate of matching and is well suited for multi-modal image registration.
Xia et al. REF combined phase congruency representations of images with scale-invariant feature transform (SIFT) to achieve multimodal medical image registration.
A robust feature-based registration method of multimodal image using phase congruency and coherent point drift
Other Conferences
Mathematics Engineering