scholarly journals A non-rigid registration method for mouse whole body skeleton registration

2010 ◽  
Author(s):  
Di Xiao ◽  
David Zahra ◽  
Pierrick Bourgeat ◽  
Paula Berghofer ◽  
Oscar Acosta Tamayo ◽  
...  
Keyword(s):  
Whole Body ◽  
Registration Method ◽  
Rigid Registration

An accurate 3D shape context based non-rigid registration method for mouse whole-body skeleton registration

2011 ◽  
Author(s):  
Di Xiao ◽  
David Zahra ◽  
Pierrick Bourgeat ◽  
Paula Berghofer ◽  
Oscar Acosta Tamayo ◽  
...  
Keyword(s):  
Whole Body ◽  
Shape Context ◽  
Registration Method ◽  
Rigid Registration ◽  
3D Shape

Constrained non-rigid registration for whole body image registration: method and validation

2007 ◽  
Author(s):  
Xia Li ◽  
Thomas E. Yankeelov ◽  
Todd E. Peterson ◽  
John C. Gore ◽  
Benoit M. Dawant
Keyword(s):  
Body Image ◽  
Image Registration ◽  
Whole Body ◽  
Registration Method ◽  
Rigid Registration

An Error-Adaptive, Non-Rigid Registration Method for the Analysis of Springback in Sheet Metal Forming

2015 ◽  
Vol 651-653 ◽  
pp. 1015-1020 ◽  
Author(s):  
Matthias Schweinoch ◽  
Alexei Sacharow ◽  
Dirk Biermann ◽  
Christoph Buchheim
Keyword(s):  
Sheet Metal ◽  
Sheet Metal Forming ◽  
Metal Forming ◽  
Simulation Software ◽  
Registration Method ◽  
Rigid Registration ◽  
Reference Shape ◽  
Geometric Deviations ◽  
Error Metric ◽  
Test Shape

Springback effects, as occuring in sheet metal forming processes, pose a challenge to manufacturingplanning: the as-built part may deviate from the desired shape rendering it unusable forits intended purpose. A compensation can be achieved by modifying the forming tools to counteractthe shape deviations. A prerequisite to compensation is the knowledge of correspondences (ui; vj),between points ui on the desired and vj on the actual shape. FEM-based simulation software providesmeans to both virtually predict springback and directly obtain correspondences. In case of experimentalprototyping and validation, however, finding correspondences requires solving a registrationproblem: given a test shape Q (scan points of the as-built geometry) and a reference shape R (CADdata of the desired geometry), a transformation S has to be found to fit both objects. Correspondencesbetween S(Q) and R may then be computed based on a metric.If S is restricted to Euclidean transformations, then S(Q) results in a rigid transformation, whereevery point of Q is subject to the same translation and rotation. Local geometric deviations due tospringback are not considered, often resulting in invalid correspondences. In this contribution, a nonrigidregistration method for the efficient analysis of springback is therefore presented. The test shape Q is iteratively partitioned into segments with respect to an error metric. The segments are locally registeredusing rigid registration subject to regulatory conditions. Resulting discontinuities are addressedby minimization of the deformation energy. The error metric uses information about the deviationscomputed based on the correspondences of the previous iteration, e.g. maximum errors or changes ofthe sign. This adaptive per-segment registration allows appropriate correspondences to be determinedeven under local geometric deviations.

scholarly journals A non-rigid registration method for the analysis of local deformations in the wood cell wall

2018 ◽  
Vol 4 (1) ◽  
Author(s):  
Alessandra Patera ◽  
Stephan Carl ◽  
Marco Stampanoni ◽  
Dominique Derome ◽  
Jan Carmeliet
Keyword(s):  
Cell Wall ◽  
Wood Cell Wall ◽  
Registration Method ◽  
Rigid Registration ◽  
Local Deformations

A three-dimensional registration method for automated fusion of micro PET-CT-SPECT whole-body images

2005 ◽  
Vol 24 (7) ◽  
pp. 886-893 ◽  
Author(s):  
Meei-Ling Jan ◽  
Keh-Shih Chuang ◽  
Guo-Wei Chen ◽  
Yu-Ching Ni ◽  
S. Chen ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Whole Body ◽  
Body Images ◽  
Registration Method ◽  
Pet Ct

A MULTISCALE PHASE FIELD METHOD FOR JOINT SEGMENTATION-RIGID REGISTRATION — APPLICATION TO MOTION ESTIMATION OF HUMAN KNEE JOINT

2011 ◽  
Vol 23 (06) ◽  
pp. 445-456 ◽  
Author(s):  
Abouzar Eslami ◽  
Fateme Esfandiarpour ◽  
Ali Shakourirad ◽  
Farzam Farahmand
Keyword(s):  
Knee Joint ◽  
Phase Field ◽  
Phase Field Method ◽  
Registration Method ◽  
Element Discretization ◽  
Rigid Registration ◽  
Rigid Objects ◽  
Human Knee Joint ◽  
Motion Parameters ◽  
Clinical Experiments

Image based registration of rigid objects has been frequently addressed in the literature to obtain an object's motion parameters. In this paper, a new approach of joint segmentation-rigid registration, within the variational framework of the phase field approximation of the Mumford-Shah's functional, is proposed. The defined functional consists of two Mumford-Shah equations, extracting the discontinuity set of the reference and target images due to a rigid spatial transformation. Multiscale minimization of the proposed functional after finite element discretization provided a sub-pixel, robust algorithm for edge extraction as well as edge based rigid registration. The implementation considerations of the proposed method, including memory usage, convergence rate and effects of parameters selection, was discussed and its efficacy was examined in a comprehensive set of synthetic, phantom and clinical experiments. It was found that the proposed joint segmentation-rigid registration method provides improved results, in comparison with the currently available methods which are often based on maximizing images similarities, especially when the reference and target images are of different qualities. A high registration accuracy was obtained when estimating the knee joint kinematics through MR images taken at different joint configurations. It was concluded that the proposed method can be effectively used in applications where 3D image registration of rigid objects is concerned, e.g. for estimation of the motion parameters of human joints.

scholarly journals An ITK Implementation of Physics-based Non-rigid Registration Method

2012 ◽  
Author(s):  
Yixun Liu ◽  
Andriy Kot ◽  
Fotios Drakopoulos ◽  
Andriy Fedorov ◽  
Andinet Enquobahrie ◽  
...  
Keyword(s):  
Block Matching ◽  
Point Selection ◽  
Registration Method ◽  
Tissue Properties ◽  
Rigid Registration ◽  
Performance Improvements ◽  
Improve Accuracy ◽  
Matching Filter ◽  
Main Components ◽  
Reduce Execution Time

As part of the ITK v4 project efforts, we have developed ITK filters for physics-based non-rigid registration (PBNRR), which satisfies the following requirements: account for tissue properties in the registration, improve accuracy compared to rigid registration, and reduce execution time using GPU and multi-core accelerators. The implementation has three main components: (1) Feature Point Selection, (2) Block Matching (mapped to both multi-core and GPU processors), and (3) a Robust Finite Element Solver. The use of multi-core and GPU accelerators in ITK v4 provides substantial performance improvements. For example, in average for the non-rigid registration of brain MRIs, the performance of the Block Matching filter is about 12 times faster when 12 hyperthreaded multi-cores are used and about 540 times faster when the Quadro 6000 with 448 threads is used in Dell Workstation.

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