Learning based coarse-to-fine image registration
This paper describes a coarse-to-fine learning based image registration algorithm which has particular advantages in dealing with multi-modality images. Many existing image registration algorithms  use a few designed terms or mutual information to measure the similarity between image pairs. Instead, we push the learning aspect by selecting and fusing a large number of features for measuring the similarity. Moreover, the similarity measure is carried in a coarse-to-fine strategy: global similarity measure is first performed to roughly locate the component, we then learn/compute similarity on the local image patches to capture the fine level information. When estimating the transformation parameters, we also engage a coarse-tofine strategy. Off-the-shelf interest point detectors such as SIFT  have degraded results on medical images. We further push the learning idea to extract the main structures/landmarks. Our algorithm is illustrated on three applications: (1) registration of mouse brain images of different modalities, (2) registering human brain image of MRI T1 and T2 images, (3) faces of different expressions. We show greatly improved results over the existing algorithms based on either mutual information or geometric structures.
Jiang, Jiayan, Songfeng Zheng, Arthur W. Toga, and Zhuowen Tu. "Learning based coarse-to-fine image registration." In 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-7. IEEE, 2008.
26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR