@article{9ce8d5e9aaab424a88cc661cdf594dad,
title = "Incorporation of Structural Tensor and Driving Force into Log-Demons for Large-Deformation Image Registration",
abstract = "Large-deformation image registration is important in theory and application in computer vision, but is a difficult task for non-rigid registration methods. In this paper, we propose a structural Tensor and Driving force-based Log-Demons algorithm for it, named TDLog-Demons for short. The structural tensor of an image is proposed to obtain a highly accurate deformation field. The driving force is proposed to solve the registration issue of large-deformation that often causes Log-Demons to trap into local minima. It is defined as a point correspondence obtained via multisupport-region-order-based gradient histogram descriptor matching on image's boundary points. It is integrated into an exponentially decreasing form with the velocity field of Log-Demons to move the points accurately and to speed up a registration process. Consequently, the driving force-based Log-Demons can well deal with large-deformation image registration. Extensive experiments demonstrate that the TDLog-Demons not only captures large deformations at a high accuracy but also yields a smooth deformation.",
keywords = "Image registration, Log-Demons algorithm, driving force, optimization, tensor",
author = "Ying Wen and Le Zhang and Lianghua He and Mengchu Zhou",
note = "Funding Information: Manuscript received June 1, 2018; revised February 4, 2019; accepted June 10, 2019. Date of publication June 26, 2019; date of current version September 4, 2019. This work was supported in part by the National Nature Science Foundation of China under Grant 61773166 and Grant 61772369, in part by the Natural Science Foundation of Shanghai under Grant 17ZR1408200, in part by the Science and Technology Commission of Shanghai Municipality under Grant 14DZ2260800, in part by the Joint Funds of the National Science Foundation of China under Grant U18092006, in part by the Changjiang Scholars Program of China, Shanghai Science and Technology Committee, under Grant 17411953100 and Grant 16JC1401300, and in part by the Shanghai Municipal Science and Technology Committee of Shanghai Outstanding Academic Leaders Plan under Grant 19XD1434000. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Gene Cheung. (Corresponding authors: LiangHua He; MengChu Zhou.) Y. Wen and L. Zhang are with the Department of Computer Science and Technology, East China Normal University, Shanghai 200062, China (e-mail: ywen@cs.ecnu.edu.cn). Publisher Copyright: {\textcopyright} 1992-2012 IEEE.",
year = "2019",
month = dec,
doi = "10.1109/TIP.2019.2924168",
language = "English (US)",
volume = "28",
pages = "6091--6102",
journal = "IEEE Transactions on Image Processing",
issn = "1057-7149",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "12",
}