Unifying framework for decomposition models of parametric and non-parametric image registration

2017 ◽  
Author(s):  
Mazlinda Ibrahim ◽  
Ke Chen
Keyword(s):  
Image Registration ◽  
Parametric Image ◽  
Decomposition Models ◽  
Non Parametric

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

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 Separability Analysis of Atlantic Forest Patches by Comparing Parametric and Non-Parametric Image Classification Algorithms

2019 ◽  
Vol 11 (05) ◽  
pp. 567-578
Author(s):  
Marcos Roberto Martines ◽  
Mariana de Paula Garcia Lúcio ◽  
Alexandre D. M. Cavagis ◽  
Marcel Fantin ◽  
Ricardo Vicente Ferreira ◽  
...  
Keyword(s):  
Image Classification ◽  
Atlantic Forest ◽  
Classification Algorithms ◽  
Parametric Image ◽  
Forest Patches ◽  
Non Parametric

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.

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