A General Fast Registration Framework by Learning Deformation-Appearance Correlation

• Published in: IEEE transactions on image processing Vol. 21; no. 4; pp. 1823 - 1833
• Main Authors: Kim, Minjeong, Wu, Guorong, Yap, Pew-Thian, Shen, Dinggang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2012
• Subjects: Accuracy; Algorithms; Artificial Intelligence; Brain modeling; Correlation; Deformable models; Deformation prediction; fast image registration; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Pattern Recognition, Automated - methods; Principal component analysis; principal component analysis (PCA); Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; support vector regression (SVR); Training
• DOI: 10.1109/TIP.2011.2170698
• ISSN: 1057-7149; 1941-0042

In this paper, we propose a general framework for performance improvement of the current state-of-the-art registration algorithms in terms of both accuracy and computation time. The key concept involves rapid prediction of a deformation field for registration initialization, which is achieved by a statistical correlation model learned between image appearances and deformation fields. This allows us to immediately bring a template image as close as possible to a subject image that we need to register. The task of the registration algorithm is hence reduced to estimating small deformation between the subject image and the initially warped template image, i.e., the intermediate template (IT). Specifically, to obtain a good subject-specific initial deformation, support vector regression is utilized to determine the correlation between image appearances and their respective deformation fields. When registering a new subject onto the template, an initial deformation field is first predicted based on the subject's image appearance for generating an IT. With the IT, only the residual deformation needs to be estimated, presenting much less challenge to the existing registration algorithms. Our learning-based framework affords two important advantages: 1) by requiring only the estimation of the residual deformation between the IT and the subject image, the computation time can be greatly reduced; 2) by leveraging good deformation initialization, local minima giving suboptimal solution could be avoided. Our framework has been extensively evaluated using medical images from different sources, and the results indicate that, on top of accuracy improvement, significant registration speedup can be achieved, as compared with the case where no prediction of initial deformation is performed.