scholarly journals GA–SSD–ARC–NLM for Parametric Image Registration

2006 ◽  
pp. 227-236 ◽  
Author(s):  
Felix Calderon ◽  
Leonardo Romero ◽  
Juan Flores
Keyword(s):  
Image Registration ◽  
Parametric Image

scholarly journals Isotropic Total Variation Regularization of Displacements in Parametric Image Registration

2017 ◽  
Vol 36 (2) ◽  
pp. 385-395 ◽  
Author(s):  
Valery Vishnevskiy ◽  
Tobias Gass ◽  
Gabor Szekely ◽  
Christine Tanner ◽  
Orcun Goksel
Keyword(s):  
Image Registration ◽  
Total Variation ◽  
Total Variation Regularization ◽  
Parametric Image

scholarly journals Adapting the ITK Registration Framework to Fit Parametric Image Models

2010 ◽  
Author(s):  
Cory Quammen ◽  
Russell M. Taylor II
Keyword(s):  
Image Registration ◽  
Gaussian Function ◽  
Input Image ◽  
Parametric Models ◽  
Parametric Image ◽  
Image Models

The image registration framework in the Insight Tookit offers a powerful body of code for finding the optimal spatial transform that registers one image with another. However, ITK currently lacks a way to fit parametric models of image pixel values to an input image. This document describes new classes that enable use of the registration framework to provide this capability. We describe a new base class, itk::ParametricImageSource, that defines an interface for parametric image sources. An adapter class itk::ImageToParametricImageSourceMetric that enables itk::ParametricImageSources to be hooked into the registration framework is also described. An example adapter class that enables the existing itk::GaussianImageSource to be used for image fitting is presented, and we demonstrate use of the classes by fitting a 2D Gaussian function to an image generated by the itk::GaussianImageSource class.

scholarly journals Software for Non-Parametric Image Registration of 2-Photon Imaging Data

2021 ◽  
Author(s):  
Philipp Flotho ◽  
Shinobu Nomura ◽  
Bernd Kuhn ◽  
Daniel J Strauss
Keyword(s):  
Computer Vision ◽  
Image Registration ◽  
Neuronal Activity ◽  
Imaging Data ◽  
Parametric Image ◽  
Signal To Noise ◽  
Key Technology ◽  
Movement Correction ◽  
Movement Artifacts ◽  
Photon Imaging

Functional 2-photon microscopy is a key technology for imaging neuronal activity which can, however, contain non-rigid movement artifacts. Despite the established performance of variational optical flow (OF) estimation in different computer vision areas and the importance of movement correction for 2-photon applications, no OF-based method for 2-photon imaging is available. We developed the easy-to-use toolbox Flow-Registration that outperforms previous alignment tools and allows to align and reconstruct even low signal-to-noise 2-photon imaging data.

scholarly journals Landmark Constrained Non-parametric Image Registration with Isotropic Tolerances

2009 ◽  
pp. 122-126 ◽  
Author(s):  
Nils Papenberg ◽  
Janine Olesch ◽  
Thomas Lange ◽  
Peter M. Schlag ◽  
Bernd Fischer
Keyword(s):  
Image Registration ◽  
Parametric Image ◽  
Non Parametric

scholarly journals Diffeomorphic demons: Efficient non-parametric image registration

NeuroImage ◽  
2009 ◽  
Vol 45 (1) ◽  
pp. S61-S72 ◽  
Author(s):  
Tom Vercauteren ◽  
Xavier Pennec ◽  
Aymeric Perchant ◽  
Nicholas Ayache
Keyword(s):  
Image Registration ◽  
Parametric Image ◽  
Non Parametric

An Image Registration Method Combing Feature Constraint with Multilevel Strategy

2011 ◽  
Vol 58-60 ◽  
pp. 286-291
Author(s):  
Hong Kui Xu ◽  
Ming Yan Jiang ◽  
Ming Qiang Yang
Keyword(s):  
Image Registration ◽  
Optimization Methods ◽  
Local Extremum ◽  
Parametric Image ◽  
Registration Accuracy ◽  
Registration Method ◽  
Structure Information ◽  
Local Difference ◽  
Intelligent Optimization Algorithms ◽  
Image Registrations

A novel method combing feature constraint with multilevel strategy to improve simultaneously the registration accuracy and speed is proposed for non-parametric image registrations. To images between which the local difference is large, integrating feature constraint constructed with local structure information of images into objective function of image registration improves the registration accuracy. When applying feature constraint under multilevel strategy, parameter searching is prevented from entrapped into local extremum by using the optimization result on coarser levels as the starting points on finer levels; meanwhile traditional optimization methods without demanding intelligent optimization algorithms which consume more time can find the accurate registration parameter on finer levels, so registration speed is improved. Experimental results indicate that this method can finish fast and accurate registration for images between which there exists large local difference.

Sign in / Sign up

Export Citation Format

Share Document