TY - JOUR
T1 - Longitudinal Image Analysis via Path Regression on the Image Manifold
AU - Ying, Shi Hui
AU - Zhang, Xiao Fang
AU - Peng, Ya Xin
AU - Shen, Ding Gang
N1 - Publisher Copyright:
© 2019, Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - Longitudinal image analysis plays an important role in depicting the development of the brain structure, where image regression and interpolation are two commonly used techniques. In this paper, we develop an efficient model and approach based on a path regression on the image manifold instead of the geodesic regression to avoid the complexity of the geodesic computation. Concretely, first we model the deformation by diffeomorphism; then, a large deformation is represented by a path on the orbit of the diffeomorphism group action. This path is obtained by compositing several small deformations, which can be well approximated by its linearization. Second, we introduce some intermediate images as constraints to the model, which guides to form the best-fitting path. Thirdly, we propose an approximated quadratic model by local linearization method, where a closed form is deduced for the solution. It actually speeds up the algorithm. Finally, we evaluate the proposed model and algorithm on a synthetic data and a real longitudinal MRI data. The results show that our proposed method outperforms several state-of-the-art methods.
AB - Longitudinal image analysis plays an important role in depicting the development of the brain structure, where image regression and interpolation are two commonly used techniques. In this paper, we develop an efficient model and approach based on a path regression on the image manifold instead of the geodesic regression to avoid the complexity of the geodesic computation. Concretely, first we model the deformation by diffeomorphism; then, a large deformation is represented by a path on the orbit of the diffeomorphism group action. This path is obtained by compositing several small deformations, which can be well approximated by its linearization. Second, we introduce some intermediate images as constraints to the model, which guides to form the best-fitting path. Thirdly, we propose an approximated quadratic model by local linearization method, where a closed form is deduced for the solution. It actually speeds up the algorithm. Finally, we evaluate the proposed model and algorithm on a synthetic data and a real longitudinal MRI data. The results show that our proposed method outperforms several state-of-the-art methods.
KW - Diffeomorphism group
KW - Image registration
KW - Infant brain development
KW - Longitudinal image analysis
KW - Path regression
UR - http://www.scopus.com/inward/record.url?scp=85067229612&partnerID=8YFLogxK
U2 - 10.1007/s40305-019-00251-2
DO - 10.1007/s40305-019-00251-2
M3 - Article
AN - SCOPUS:85067229612
SN - 2194-668X
VL - 7
SP - 599
EP - 614
JO - Journal of the Operations Research Society of China
JF - Journal of the Operations Research Society of China
IS - 4
ER -