Results 151 to 160 of about 30,960 (188)

Image registration with local deformation

IEEE APCCAS 2000. 2000 IEEE Asia-Pacific Conference on Circuits and Systems. Electronic Communication Systems. (Cat. No.00EX394), 2002
This research is focused on the study of local-type image registration techniques, which concern the establishment of transform mapping between pixels of two correlated images with local deformation. The proposed algorithm is actually hybrid that locally dynamic corrective matching and global surface-spline fitting techniques are combined.
null Wen-Nung Lie, null Cheng-Hung Chuang, null Yuh-Hung Lee   +2 more
openaire   +1 more source

Deformable Image Registration Based on Functions of Bounded Generalized Deformation

International Journal of Computer Vision, 2021
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Ziwei Nie, Chen Li, Hairong Liu, Xiaoping Yang   +3 more
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Deformable registration of digital images

Journal of Computer Science and Technology, 1998
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guan, Weiguang, Xie, Lin, Ma, Songde
openaire   +2 more sources

Directly Manipulated Free-Form Deformation Image Registration

IEEE Transactions on Image Processing, 2009
Previous contributions to both the research and open source software communities detailed a generalization of a fast scalar field fitting technique for cubic B-splines based on the work originally proposed by Lee . One advantage of our proposed generalized B-spline fitting approach is its immediate application to a class of nonrigid registration ...
Nicholas J, Tustison, Brian B, Avants, James C, Gee   +2 more
openaire   +2 more sources

Graph-based Deformable Image Registration

2015
Deformable image registration is a field that has received considerable attention in the medical image analysis community. As a consequence, there is an important body of works that aims to tackle deformable registration. In this chapter we review one class of these techniques that use discrete optimization, and more specifically Markov Random Field ...
A. Sotiras, Y. Ou, N. Paragios, C. Davatzikos   +3 more
openaire   +1 more source

Inverse Consistent Deformable Image Registration

2010
This paper presents a novel variational model for inverse consistent deformable image registration. The proposed model deforms both source and target images simultaneously, and aligns the deformed images in the way that the forward and backward transformations are inverse consistent. To avoid the direct computation of the inverse transformation fields,
Yunmei Chen, Xiaojing Ye
openaire   +1 more source

Large Deformation Inverse Consistent Elastic Image Registration

2003
This paper presents a new image registration algorithm that accommodates locally large nonlinear deformations. The algorithm concurrently estimates the forward and reverse transformations between a pair of images while minimizing the inverse consistency error between the transformations.
Jianchun, He, Gary E, Christensen
openaire   +2 more sources

Local statistical deformation models for deformable image registration

Neurocomputing, 2018
Abstract A fast and robust image registration algorithm for high-dimensional brain Magnetic Resonance images was developed based on the statistical deformation models (SDMs). This model learns deformation fields and achieves fast and robust registration by greatly reducing transformation dimensionality.
Songyuan Tang, Weijian Cong, Jian Yang, Tianyu Fu, Hong Song, Danni Ai, Yongtian Wang   +6 more
openaire   +1 more source

On Combining Algorithms for Deformable Image Registration

2012
We propose a meta-algorithm for registration improvement by combining deformable image registrations (MetaReg). It is inspired by a well-established method from machine learning, the combination of classifiers. MetaReg consists of two main components: (1) A strategy for composing an improved registration by combining deformation fields from different ...
Muenzing, S.E.A., Ginneken, van, B., Pluim, J.P.W.   +2 more
openaire   +1 more source

Multiscale unsupervised network for deformable image registration

Journal of X-Ray Science and Technology
BACKGROUND: Deformable image registration (DIR) plays an important part in many clinical tasks, and deep learning has made significant progress in DIR over the past few years. OBJECTIVE: To propose a fast multiscale unsupervised deformable image registration (referred to as FMIRNet) method for monomodal image registration.
Yun, Wang, Wanru, Chang, Chongfei, Huang, Dexing, Kong   +3 more
openaire   +2 more sources