Loading [a11y]/accessibility-menu.js
Learning based coarse-to-fine image registration | IEEE Conference Publication | IEEE Xplore

Learning based coarse-to-fine image registration


Abstract:

This paper describes a coarse-to-fine learning based image registration algorithm which has particular advantages in dealing with multi-modality images. Many existing ima...Show More

Abstract:

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 [18] 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-to-fine strategy. Off-the-shelf interest point detectors such as SIFT [12] 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.
Date of Conference: 23-28 June 2008
Date Added to IEEE Xplore: 05 August 2008
ISBN Information:
Print ISSN: 1063-6919
Conference Location: Anchorage, AK, USA

References

References is not available for this document.