Elements of information theory
Elements of information theory
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Affine Registration with Feature Space Mutual Information
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Likelihood maximization approach to image registration
IEEE Transactions on Image Processing
Multimodality Image Registration Using Spatial Procrustes Analysis and Modified Conditional Entropy
Journal of Signal Processing Systems
Parametric estimation of affine deformations of planar shapes
Pattern Recognition
Multi-modality image registration using gradient vector flow intensity
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
| Hi-index | 0.00 |
In this paper, a novel one-element voxel attribute, namely distance-intensity (DI), is defined for associating spatial information with image intensity for registration tasks. For each voxel in an image, the DI feature encodes spatial information at a global level, and is about the distance of the voxel to its closest object boundary, together with the original intensity information. Without the help of image segmentations, the computation of the DI map is carried out by applying a Poisson process on a vector field that combines both gradient and distance-gradient. Mutual information (MI) is adopted as a similarity measure on the DI feature space. A multi-resolution registration method is then used for aligning multi-modal images. Experimental results show that, as compared with the conventional MI-based method, the proposed method has longer capture ranges at different image resolutions. This leads to more robust registrations. Randomized registration experiments on clinical 3D CT, MR-T1 and MR-T2 datasets demonstrate that the new method gives higher success rates than the traditional MI-based method.