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 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 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 An image sampling framework for the ITK

2010 ◽  
Author(s):  
Marius Staring ◽  
Stefan Klein
Keyword(s):  
Image Registration ◽  
Similarity Measure ◽  
Input Data ◽  
Source Code ◽  
Input Image ◽  
Output Data ◽  
Image Intensity ◽  
Image Sampling ◽  
A Value ◽  
Voxel Grid

This document describes the implementation of image samplers using the Insight Toolkit ITK url{www.itk.org}. Image samplers take a set of `picks’ from an image and store them in an array. A sample consists of the location of the pick (a point) and the corresponding image intensity (a value). Image samplers are useful for image registration, where samples are drawn from the fixed image in order to compute the similarity measure. Together with an image sampler base class, we introduce the following image samplers: 1) a full sampler that draws all voxel coordinates from the input image, 2) a grid sampler that draws samples from a user-specified regular voxel grid, 3 and 4) two random samplers that uniformly draw a user-specified number of samples from the input image.This paper is accompanied with the source code, input data, parameters and output data that the authors used for validating the algorithm described in this paper.

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
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