A relatively new family of image registration methods, namely groupwise registration, has recently emerged and been widely investigated due to its fundamentally key role in analyzing image populations in terms of atla...A relatively new family of image registration methods, namely groupwise registration, has recently emerged and been widely investigated due to its fundamentally key role in analyzing image populations in terms of atlas-based analysis or clinical diagnostic systems. Compared with pairwise registration, groupwise registration is capable of handling a large-scale population of images simultaneously in an unbiased way. In this paper, a review of the latest research on groupwise registration is presented. First, the schemes of pairwise registration and groupwise registration are compared. Then, a classification of groupwise registration and several exemplar implementations of groupwise registration are illustrated, including their experimental results. Finally, typical applications of groupwise registration, e.g., infant atlas construction, and population-based anatomical variability evaluation, are discussed in this paper.展开更多
Existing groupwise dimension reduction requires given group structure to be non-overlapped. This confines its application scope. We aim at groupwise dimension reduction with overlapped group structure or even unknown ...Existing groupwise dimension reduction requires given group structure to be non-overlapped. This confines its application scope. We aim at groupwise dimension reduction with overlapped group structure or even unknown group structure. To this end, existing groupwise dimension reduction concept is extended to be compatible with overlapped group structure. Then, the envelope method is ameliorated to deal with overlapped groupwise dimension reduction. As an application, Gaussian graphic model is employed to estimate the structure between predictors when the group structure is not given, and the amended envelope method is used for groupwise dimension reduction with graphic structure. Furthermore, the rationale of the proposed estimation procedure is explained at the population level and the estimation consistency is proved at the sample level. Finally, the finite sample performance of the proposed methods is examined via numerical simulations and a body fat data analysis.展开更多
文摘A relatively new family of image registration methods, namely groupwise registration, has recently emerged and been widely investigated due to its fundamentally key role in analyzing image populations in terms of atlas-based analysis or clinical diagnostic systems. Compared with pairwise registration, groupwise registration is capable of handling a large-scale population of images simultaneously in an unbiased way. In this paper, a review of the latest research on groupwise registration is presented. First, the schemes of pairwise registration and groupwise registration are compared. Then, a classification of groupwise registration and several exemplar implementations of groupwise registration are illustrated, including their experimental results. Finally, typical applications of groupwise registration, e.g., infant atlas construction, and population-based anatomical variability evaluation, are discussed in this paper.
基金supported by a grant from the University Grant Council of Hong Kong of ChinaNational Natural Science Foundation of China (Grant No. 11371013)Tian Yuan Foundation for Mathematics
文摘Existing groupwise dimension reduction requires given group structure to be non-overlapped. This confines its application scope. We aim at groupwise dimension reduction with overlapped group structure or even unknown group structure. To this end, existing groupwise dimension reduction concept is extended to be compatible with overlapped group structure. Then, the envelope method is ameliorated to deal with overlapped groupwise dimension reduction. As an application, Gaussian graphic model is employed to estimate the structure between predictors when the group structure is not given, and the amended envelope method is used for groupwise dimension reduction with graphic structure. Furthermore, the rationale of the proposed estimation procedure is explained at the population level and the estimation consistency is proved at the sample level. Finally, the finite sample performance of the proposed methods is examined via numerical simulations and a body fat data analysis.
