Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Ordinal Palmprint Represention for Personal Identification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Non-rigid image registration of brain magnetic resonance images using graph-cuts
Pattern Recognition
Segmenting images by combining selected atlases on manifold
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Face recognition using ordinal features
ICB'06 Proceedings of the 2006 international conference on Advances in Biometrics
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems
IEEE Transactions on Evolutionary Computation
Fundamental performance limits in image registration
IEEE Transactions on Image Processing
Embedded palmprint recognition system on mobile devices
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Visible and infrared image registration in man-made environments employing hybrid visual features
Pattern Recognition Letters
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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.