ABSORB: Atlas Building by Self-organized Registration and Bundling

To achieve more accurate and consistent registration in an image population, a novel hierarchical groupwise registration framework, called Atlas Building by Self-Organized Registration and Bundling (ABSORB), is proposed in this paper. In this new framework, the global structure, i.e., the relative distribution of subject images is always preserved during the registration process by constraining each subject image to deform only locally with respect to its neighbors within the learned image manifold. To achieve this goal, two novel strategies, i.e., the self-organized registration by warping one image towards a set of its eligible neighbors and image bundling to cluster similar images, are specially proposed. By using these two strategies, this new framework can perform groupwise registration in a hierarchical way. Specifically, in the high level, it will perform on a much smaller dataset formed by the representative subject images of all subgroups that are generated in the previous levels of registration. Compared to the other groupwise registration methods, our proposed framework has several advantages: (1) it explores the local data distribution and uses the obtained distribution information to guide the registration; (2) the possible registration error can be greatly reduced by requiring each individual subject to move only towards its nearby subjects with similar structures; (3) it can produce a smoother registration path, in general, from each subject image to the final built atlas than other groupwise registration methods. Experimental results on both synthetic and real datasets show that the proposed framework can achieve substantial improvements, compared to the other two widely used groupwise registration methods, in terms of both registration accuracy and robustness.