![]() When you want the plugin to perform the registration press “OK”. If you want to exit the plugin, press “Cancel”. RGB Color images will be converted to grayscale during the registration process but the resulting transformations will be applied to the original color images. The stop threshold is used by the algorithm to stop the optimization process at each multiresolution level when the error relative change is not larger than this threshold. Methods and Applications.”, given at the Second ImageJ User & Developer Conference (2008) or have a look at the FAQ section. You can see a description of the different function weights in the presentation “ bUnwarpJ: Consistent and Elastic Registration in ImageJ. These weights are not restricted to be between 0 and 1, and they may take any value as long as it is non-negative. The different weights of the goal function control the relative weight of each one of the terms. This is very useful when registering large images. the image dimensions can be reduced by a factor of 2 0 = 1 to 2 7 = 128. The image subsampling parameter can be chosen between 0 and 7, i.e. The registration will be then calculated using the subsampled versions of the images but the results will be applied to the original ones. Since bUnwarpJ 2.5 there is a new parameter on the main window to allow subsampling the input images. As you increase the deformation level, the number of splines is doubled in each direction (horizontal and vertical). “Very coarse” corresponds to 4 splines (one in each corner of the image). ![]() The “Initial” and “Final” deformation lists allow you to select the coarsest and finest scale of the spline deformation field. This panel gives you access to most of the internal parameters of the algorithm. More internal options can be modified in the “Advanced Options” panel. The two registration modes “Accurate” and “Fast” involve performing bidirectional registration and affect the stopping criteria internally used by the program. The registration mode “ Mono” (included since version 2.5) makes the program to perform only unidirectional registration, i.e. The registration mode can be “Accurate”, “Fast” and “Mono”. If so, the maing dialog window of the plugin will open.īoth selected images will work simultaneously as source and target, their tags are there only for the sake of clarification. ![]() Two images ( 8, 16, 32-bit grayscale or RGB Color) need to be opened in order to be able to use the plugin. The plugin can be called from the main ImageJ/Fiji menu under Plugins › Registration › bUnwarpJ. The optimization process is a Levenberg–Marquardt algorithm minimization enhanced by a Broyden–Fletcher–Goldfarb–Shanno algorithm estimate of the local Hessian of the goal function, and both, images and deformations are represented by B-spline. Where the weights of every term are set by the user in the main window of the plugin. This image registration algorithm is based on the minimization of an energy functional that includes the dissimilarity between the source and target images -in both directions- \(E_\] Two images are given as a result: the deformed versions of A and B images. Image A is elastically deformed in order to look as similar as possible to image B, and, at the same time, the “inverse” transformation (from B to A) is also calculated so a pseudo-invertibility of the final deformation could be guaranteed. It performs a simultaneous registration of two images, A and B. General DescriptionīUnwarpJ is an algorithm for elastic and consistent image registration developed as an ImageJ plugin. The invertibility of the deformations is enforced through a consistency restriction.įor a quick start, you can have a look at the video tutorial (awarded at the Second ImageJ User & Developer Conference). This ImageJ/Fiji plugin performs 2D image registration based on elastic deformations represented by B-splines. If you’d like to help, check out the how to help guide!īUnwarpJ scheme: bidirectional Unwarping in Java. The content of this page has not been vetted since shifting away from MediaWiki.
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