Multimodality image registration using local linear embedding and hybrid entropy

Robust registration of multimodality medical images has become an active area of research in medical image processing and applications. Although mutual information (MI) has been successfully applied to image registration, it is worth noting that MI-based registration measure only takes statistical intensity information into account and does not use spatial features. In this paper, we propose a local linear embedding (LLE) and hybrid entropy based registration method that combines spatial information into registration measure. Due to the robustness to absolute intensity information of image pixels and stability in noisy environment, the ordinal features (OFs) with different orientations are extracted to represent spatial information in medical images. For high dimensional OFs, the LLE algorithm is used to dimensionality reduction and the inverse mapping of LLE is used to fuse complementary information of OFs together. Then a novel similarity measure based on hybrid entropy which integrates intensity with OF is defined to register multimodality images. The experimental results show that the proposed registration algorithm can effectively suppress and eliminate the influence of noise in images. Compared with some existing methods, the proposed algorithm is of higher precision and better robustness.