diff --git a/crop_pan_zoom.md b/crop_pan_zoom.md deleted file mode 100644 index ae5cc96..0000000 --- a/crop_pan_zoom.md +++ /dev/null @@ -1,22 +0,0 @@ -# Crop, Pan & Zoom -When optimizing workflows, is might make sense to do that on a sub-volume of a bigger stack, just to spare time while optimizing parameters. -Therefore, there is a Crop operation for cropping a sub-region. This sub-region can be panned while more operations are -attached to its result. Furthermore, there is a Zoom operation for inspecting results in more detail. - -![Image](images/assistant_crop_pan_zoom.png) - -## How to crop, pan and zoom -Open your data set. [Start the CLIJx-Assistant](https://clij.github.io/assistant/getting_started) and follow these steps: - -* Your dataset - * CLIJx-Assistant Starting point - * Crop - * [Optional: Post-processing, such as Background subtraction] - * Zoom - - -[Download video](images/assistant_crop_pan_zoom.mp4) [Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/cylinder_projection.md b/cylinder_projection.md deleted file mode 100644 index f15e08b..0000000 --- a/cylinder_projection.md +++ /dev/null @@ -1,39 +0,0 @@ -# Cylinder projection -The cylinder projection is typically used to project intensities from a spherical or cylindrical sample into a 2D image. - -![Image](images/cylinder_projection.png) - -## How to use a cylinder projection on your data -When applying cylinder projections to image stacks, a rigid dransform in advance is helpful to have control of the position and tilt of your sample in projected space. - - -Open your time lapse data set. [Start the CLIJx-Assistant](https://clij.github.io/assistant/getting_started) and follow these steps: - -* Your dataset - * CLIJx-Assistant Starting point - * [Optional: Noise removal and Background subtraction] - * Make Isotropic - * Rigid transform - * Cylinder transform - * Maximum Z projection - - -[Download video](images/cylinder_transform_drosophila.mp4) - -## Half-cylinder projection -If just half of a sample was imaged, you may want to apply a half-cylinder projection. -In the maximum projection, full-cylinder projections of half-embryos also look suspicious: - -![Image](images/cylinder_half_tribolium1.png) - -You can turn a full-cylinder projection by changing the center of the transform and the number of angles to 180 or the angle step to 0.5 degrees. - -![Image](images/cylinder_half_tribolium2.png) - -The detailed procedure is shown in this video: - -[Download video](images/cylinder_half_tribolium.mp4) [Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/filtering.md b/filtering.md deleted file mode 100644 index 318282d..0000000 --- a/filtering.md +++ /dev/null @@ -1,30 +0,0 @@ -# Filtering -CLIJx-Assistant comes with filters for -* Noise removal - * Mean - * Median - * Gaussian blur -* Background subtraction - * Top Hat (similar to ImageJs "Subtract Background") - * Subtract Gaussian background -* mixed - * Difference of Gaussian (DoG) - * Laplacien of Gaussian (LoG) -* Intensity correction - * [Gamma correction](https://clij.github.io/assistant/gamma_correction) - * Intensity correction - -... and more - - -## How to apply filters to your data -Open your data set. [Start the CLIJx-Assistant](https://clij.github.io/assistant/getting_started). -To configure the filter of your choice with the optimal settings, also activate following processing steps, -such as thresholding. Instant feedback allows you to study consequences of different parameter settings. - - -[Download video](images/incubator_filtering_with_thresholding.mp4) - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/gamma_correction.md b/gamma_correction.md deleted file mode 100644 index fbf0a1c..0000000 --- a/gamma_correction.md +++ /dev/null @@ -1,17 +0,0 @@ -# Gamma correction -CLIJx-Assistant comes a gamma correction filter. Note: Gamma correction is useful -for visualisation or as a preprocessing step for segmentation. -Be careful with intensity measurements from gamma-corrected images. -It is recommended to measure intensity in the original images. - -## How to apply gamma correction -Open your data set. [Start the CLIJx-Assistant](https://clij.github.io/assistant/getting_started). -Click the right-click menu `Filter > Gamma correction`. Use the main menu `Analyze > Histogram` and click its -`Live` button to see what happens while changing the gamma - - -[Download video](images/gamma_correction.mp4) - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/getting_started.md b/getting_started.md deleted file mode 100644 index 60a6266..0000000 --- a/getting_started.md +++ /dev/null @@ -1,44 +0,0 @@ -# Getting started designing image processing workflows with the CLIJx Assistant -Open your 3D+channel+time data set. It's recommended to start the assistant from a file that has been loaded from or saved to disk. -Afterwards, activate CLIJx-Assistant by clicking on its tool icon. - -![Image](images/installation_ok.png) - -## Building workflows - step by step -CLIJx-Assistant has a built-in suggestions of what to do next: -Just right click in any image that has the assistant attached. - -![Image](images/suggestion_make_isotropic.png) -[Image data source: Irene Seijo Barandiaran, Grapin-Botton lab, MPI CBG] - -Consider the suggestions but also explore the categories of all available operations. - -![Image](images/menu_rigid_tranform.png) -[Image data source: Irene Seijo Barandiaran, Grapin-Botton lab, MPI CBG] - -You also find all CLIJx-Assistant operations in Fijis search bar. They have their own category to not be mixed up with -CLIJ2 operations: - -![Image](images/fiji_search.png) - -## Interoperability with classical ImageJ and Fiji operations -As CLIJx-Assistant runs in classical ImageJ windows, you can use ImageJ operations on the shown images. -However, they may be overwritten as soon as CLIJx-Assistant recomputes its results. -Thus, it is recommended to duplicate an image before applying classical functions to it. -You can use ImageJ's `Duplicate...` menu or the built in menu: - -![Image](images/interoperability_imagej.png) - -## The assistant in action - -[Download video](images/basic_usage.mp4) - -If you want to keep an eye on memory usage in the GPU, -the menu `Plugins > ImageJ on GPU (CLIJx) > Memory Display` allows you to overview available memory and memory consumption while building your workflow. - -![Image](images/memory_display.png) - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) - diff --git a/icy_protocol_export.md b/icy_protocol_export.md deleted file mode 100644 index 7541655..0000000 --- a/icy_protocol_export.md +++ /dev/null @@ -1,15 +0,0 @@ -# Icy / clIcy export -Workflows can be exported as Icy Protocols. - -## Installation -If you want to conveniently open Icy Protocols directly from Fiji, -please follow the corresponding [installation instructions](https://clij.github.io/assistant/installation#icy). - -## Usage -In order to export a workflow as protocol to Icy, click the right mouse button on any -assistant window and click the menu `Generate Script > Icy Protocol`. - -Please note: Export to Icy Workflows is limited to operations from the CLIJ2 library at the moment. - - -[Download video](images/icy_export.mp4) diff --git a/images/Sphere_projection_Tribolium_Dunkirk.gif b/images/Sphere_projection_Tribolium_Dunkirk.gif deleted file mode 100644 index ed39ba8..0000000 Binary files a/images/Sphere_projection_Tribolium_Dunkirk.gif and /dev/null differ diff 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b/installation.md deleted file mode 100644 index 6f1085a..0000000 --- a/installation.md +++ /dev/null @@ -1,174 +0,0 @@ -# CLIJx-Assistant installation instructions -CLIJx-Assistant is under development. Please install it only in a separate Fiji installation. -Do not use it for routine research yet. Planned release is summer 2021. Stay tuned. - - -## Installation in Fiji -* Download and unpack [Fiji](https://fiji.sc) -* Start Fiji and run its update using the menu `Help > Update` - -![Image](images/install_fiji_menu.png) - -* Click on "Manage update sites" and activate the _three_ updates sites "clij", "clij2" and "clijx-assistant". - -![Image](images/installation.png) - -* Click on "Close" -* Click on "Apply Changes" -* Restart Fiji - -Installation was successful if you find the CLIJx-Assistant starting point button in Fijis tool bar: - -![Image](images/installation_ok.png) - - -## Optional: Extensions -There are extensions available, e.g. for -[BoneJ](https://bonej.org), -[ImageJ](https://imagej.nih.gov/ij/index.html), -[ImageJ2](https://imagej.net), -[ImageJ 3D Suite](https://imagejdocu.tudor.lu/plugin/stacks/3d_ij_suite/start), -[Imglib2](https://github.com/imglib/imglib2/), -[MorphoLibJ](https://github.com/ijpb/MorphoLibJ) and -[SimpleITK](https://simpleitk.org). To make use of them, -please also activate these update sites: -* clijx-assistant-extensions -* 3D ImageJ Suite -* BoneJ -* IJPB-Plugins - - -## Installation in ImageJ -Download and upack [ImageJ](https://imagej.nih.gov/ij/download.html) and some jar-files: - -* [clij2-imagej_-2.2.0.19-jar-with-dependencies.jar](https://github.com/clij/clij2-imagej1/releases/download/2.2.0.19/clij2-imagej_-2.2.0.19-jar-with-dependencies.jar) -* [clijx-weka_-0.30.1.19.jar](https://github.com/clij/clijx/releases/download/0.30.1.19/clijx-weka_-0.30.1.19.jar) -* [clijx_-0.30.1.19.jar](https://github.com/clij/clijx/releases/download/0.30.1.19/clijx_-0.30.1.19.jar) -* [clijx-assistant_-0.4.2.19.jar](https://github.com/clij/assistant/releases/download/0.4.2.19/clijx-assistant_-0.4.2.19.jar) - -Please put these jar files in the `plugins` sub-directory of ImageJ: - -![Image](images/installation_imagej.png) - -Installation in ImageJ was ok, if the menu `Plugins > ImageJ on GPU (CLIJx-assistant)` appeared. -Click on `Start CLIJx-Assistant` to try it out. -If a window with a green frame appears, it worked! -![Image](images/installation_imagej_ok.png) - - -## Installation in MicroManager 2 -Download and install [MicroManager 2](https://micro-manager.org/wiki/Download_Micro-Manager_Latest_Release) and some jar files: - -* [clij2-imagej_-2.2.0.19-jar-with-dependencies.jar](https://github.com/clij/clij2-imagej1/releases/download/2.2.0.19/clij2-imagej_-2.2.0.19-jar-with-dependencies.jar) -* [clijx-weka_-0.30.1.19.jar](https://github.com/clij/clijx/releases/download/0.30.1.19/clijx-weka_-0.30.1.19.jar) -* [clijx_-0.30.1.19.jar](https://github.com/clij/clijx/releases/download/0.30.1.19/clijx_-0.30.1.19.jar) -* [clijx-assistant_-0.4.2.19.jar](https://github.com/clij/assistant/releases/download/0.4.2.19/clijx-assistant_-0.4.2.19.jar) - -Please put these jar files in the `plugins\Micro-Manager` sub-directory of MicroManager: -![Image](images/installation_mm.png) - -Start MicroManager and check if the installation worked as in the [ImageJ](https://clij.github.io/assistant/installation#imagej) section above. -During multi-dimenionsal acquisition, just Start CLIJx-Assistant and use the right click menu to setup a workflow: -![Image](images/micromanager.gif) - - -## Troubleshooting -In case of any issues, please refer to the more detailed [installation instructions of CLIJ2](https://clij.github.io/clij2-docs/installationInFiji) and -the [trouble shooting](https://clij.github.io/clij2-docs/troubleshooting) section. - - -## Windows specific installation -Windows users may have to install graphics cards drivers downloaded from the vendors website ([AMD](https://www.amd.com/en/support), [NVidia](https://www.nvidia.com/Download/index.aspx)). The driver delivered by Windows Update is not sufficient. - - -## MacOS specific installation -When using an AMD graphics card in recent Macs, you may want to turn **OFF** the option "Automatic graphics switching" under System Preferences / Energy Saver. Thanks to [Tanner Fadero for finding this out]( -https://forum.image.sc/t/ijm-macro-crashes-after-a-few-loops/40130/17). - - -## Linux specific installation -Also under linux, installation of drivers from the vendors website is necessary ([AMD](https://www.amd.com/en/support), [NVidia](https://www.nvidia.com/Download/index.aspx)). Furthermore, when working with Intel graphics, it might be necessary to install packages such as "ocl-icd-devel" to make Fiji / CLIJ discover the right GPU devices. - - -## Optional: ImageJ Macro Markdown Installation -If you want to export your workflows notebook style with [ImageJ Macro Markdown](https://github.com/haesleinhuepf/imagejmacromarkdown), -please also activate the "IJMMD" update site in Fiji. - - -## Optional: Plugin generator Installation -In order to build Fiji plugins, please install Java Development kit, version 8 or higher, e.g. [OpenJDK](https://openjdk.java.net/). -Furthermore, please download and install [git](https://git-scm.com/) and [maven](https://maven.apache.org/). -For technical reasons, the `/bin/` folder of the `git` installation must be added to the PATH of the operating system. -Furthermore, it is recommended to add maven to the path as well. -(How to: -[Windows](https://answers.microsoft.com/en-us/windows/forum/windows_10-other_settings/adding-path-variable/97300613-20cb-4d85-8d0e-cc9d3549ba23) -[Linux](https://opensource.com/article/17/6/set-path-linux) -[MacOS](https://support.apple.com/guide/terminal/use-environment-variables-apd382cc5fa-4f58-4449-b20a-41c53c006f8f/mac) -). - -After installing git, maven and the JDK, please enter appropriate paths under `Plugins > ImageJ on GPU (CLIJx-Assistant) > Options > Build and Run options` - -![Image](images/build_and_run_options.png) - -The above mentioned tools allow you to compile Fiji plugins. In order to edit the code conveniently, -it is recommended to install an Integrated Development Environment (IDE) such as IntelliJ or Eclipse. - - -## Optional: C++ compilation (CLIc / clEsperanto) -If you want to implement workflows using CLIc in C++, please follow the installation instructions [here](https://github.com/clEsperanto/CLIc_prototype). - - - -## Optional: Icy Protocol bridge -For exporting workflows to Icy Protocols, it might be handy to start Icy from ImageJ. Therefore, [download and install Icy](http://icy.bioimageanalysis.org/download/) -and enter its location under `Plugins > ImageJ on GPU (CLIJx-Assistant) > Options > Build and Run options`. - - -## Optional: Te Oki / Python Installation -If you want to run Python and/or Napari from Fiji, please install a conda environment, e.g. via [mini-conda](https://docs.conda.io/en/latest/miniconda.html). - -Using conda, create a conda environment e.g. named te_oki: -``` -conda create --name te_oki -``` -Activate the environment: -``` -conda activate te_oki -``` -Install dependencies, either via conda install or -``` -pip install pyopencl napari ipython matplotlib numpy pyclesperanto_prototype scikit-image jupyter -``` -Within this environment you can run generated [clEsperanto](https://clesperanto.github.io/) python scripts. - -If the name of your conda environment differs, please configure it under `Plugins > ImageJ on GPU (CLIJx-Assistant) > Options > Build and Run options` - - -## Optional: CLIJPY / PyImageJ / Python Installation -For running pyimagej python code, please follow the installation instructions on the [pyimagej github page](https://github.com/imagej/pyimagej). - - -## Hardware requirements -CLIJx-Assistant uses modern graphics cards to ensure real-time image processing experience. -Therefore, it is recommended to utilize state-of-the art graphics computing units (GPUs). -When considering purchasing modern GPUs, please take into account: -* **Memory size**: As image processing is [memory-bound](https://en.wikipedia.org/wiki/Memory_bound_function) look out for GPUs with -large memory. For typical scenarios, it is recommended to buy GPUs with at least 8 GB of GDDR6 RAM memory. -* **Memory Bandwidth**: GPU vendors specify their products computing capabilities with various terminology and metrics. - Look out for memory bandwidth: typical GDDR5-based GPUs have a memory bandwidth < 100 GB/s. - Quite some GDDR6 GPUs for example offer > 400 GB/s. - Thus, GDDR6-based GPUs may compute image processing results about 4 times faster! -* **Integrated GPUs**: If you desire processing images in long workflows, it might make sense to use integrated GPUs with access to huge amounts of DDR4-memory. -They are more affordable. - -You can check the capabilities of your graphics processing units by selecting a device using the menu `Plugins > ImageJ on GPU (CLIJx) > Change default CL device``` - -![Image](images/default_cl_device.png) - -The menu `Plugins > ImageJ on GPU (CLIJx) > Memory Display` allows you to overview available memory and memory consumption while building your workflow. - -![Image](images/memory_display.png) - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/intensity_projection.md b/intensity_projection.md deleted file mode 100644 index ac63f87..0000000 --- a/intensity_projection.md +++ /dev/null @@ -1,34 +0,0 @@ -# Intensity projection -CLIJx-Assistant has these intensity projections in Z available: -* Maximum Z Projection -* Minimum Z Projection -* Mean Z Projection -* Mean Z Projection of pixels above a given threshold -* Median Z Projection -* Standard Deviation Z Projection - -You can increase contrast in visualisation of your sample by executing [background subtraction](https://clij.github.io/assistant/filtering) in advance. - -## How to do intensity projections -Open your time lapse data set. [Start the CLIJx-assistant](https://clij.github.io/assistant/getting_started) and follow these steps: - -* Your dataset - * CLIJx-Assistant Starting point - * Background subtraction - * Maximum Z Projection - -Furthermore, if you want to project in different directions, consider these methods: -* Reslice Left -* Reslice Right -* Reslice Top -* Reslice Bottom -* Transpose XY -* Transpose XZ -* Transpose YZ - - -[Download video](images/incubator__working_principle.mp4) [Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/macro_export.md b/macro_export.md deleted file mode 100644 index 0a9c5d3..0000000 --- a/macro_export.md +++ /dev/null @@ -1,23 +0,0 @@ -# Exporting workflows as ImageJ Macro -After you finished designing your image analysis workflow, you can export an ImageJ script and apply it to image sequences systematically. - -Several languages are supported. Furthermore, you can export a script in multiple languages and compare: - -![Image](images/script_language_comparison.png) - - -[Download video](images/incubator_generate_macro.mp4) [Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] - -# Exporting as ImageJ Macro Markdown notebook -If you want to export your workflow as ImageJ Macro Markdown notebook, please follow the -[installation instructions](https://clij.github.io/assistant/installation#ijmmd) - -[Download video](images/clijxa_imagej_macro_markdown.mp4) - - -**Please note:** While CLIJx-Assistant is running, the GPU may be busy and full of images. -Thus, before running your generated macro, close all CLIJx-Assistant windows. - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/multi_channel_support.md b/multi_channel_support.md deleted file mode 100644 index d206755..0000000 --- a/multi_channel_support.md +++ /dev/null @@ -1,23 +0,0 @@ -# Multi-channel visualisation -Multi-channel image data is supported although increased memory consumption may be observed. -In general, all operations available in CLIJx-Assistant also process multi-channel data. -However, for typical [image segmentation](https://clij.github.io/assistant/segmentation_nuclei) workflows, it is recommended to extract a single channel, eg. before segmenting the image. - -It is recommended to utilize modern [GDDR6-based GPU hardware](https://clij.github.io/assistant/installation#hardware) for 3D visualisation. - -## How to visualize multi-channel 3D data -Open your data set. [Start the CLIJx-Assistant](https://clij.github.io/assistant/getting_started), ensure that pixel/voxel size is correctly configured and follow such a workflows: - -* Your dataset - * CLIJx-Assistant Starting point - * Make isotropic - * Rigid Transform - * Maximum Z Projection - - -[Download video](images/incbator_multichannel.mp4) [Image data source: Johannes Girstmair, Tomancak lab, MPI CBG] - - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/neighbor_analysis.md b/neighbor_analysis.md deleted file mode 100644 index 53c3366..0000000 --- a/neighbor_analysis.md +++ /dev/null @@ -1,6 +0,0 @@ -## Neighbor analysis - -todo... :-) - -![Image](images/neighbor_analysis.png) - diff --git a/neighbor_analysis_generated.md b/neighbor_analysis_generated.md deleted file mode 100644 index d8da276..0000000 --- a/neighbor_analysis_generated.md +++ /dev/null @@ -1,108 +0,0 @@ -## Neighbor analysis - -todo... :-) - -![Image](images/neighbor_analysis.png) - -* [Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_averageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids and replaces every label with the average distance to the n closest neighboring labels. - -* [Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_averageNeighborDistanceMap) -Takes a label map, determines which labels touch and replaces every label with the average distance to their neighboring labels. - -* [Draw Distance Mesh Between Touching Labels](https://clij.github.io/clij2-docs/reference_drawDistanceMeshBetweenTouchingLabels) -Starting from a label map, draw lines between touching neighbors resulting in a mesh. - -* [Draw Mesh Between Touching Labels](https://clij.github.io/clij2-docs/reference_drawMeshBetweenTouchingLabels) -Starting from a label map, draw lines between touching neighbors resulting in a mesh. - -* [Draw Touch Count Mesh Between Touching Labels](https://clij.github.io/clij2-docs/reference_drawTouchCountMeshBetweenTouchingLabels) -Starting from a label map, draw lines between touching neighbors resulting in a mesh. - -* [Draw Touch Portion Mesh Between Touching Labels](https://clij.github.io/clij2-docs/reference_drawTouchPortionMeshBetweenTouchingLabels) -Starting from a label map, draw lines between touching neighbors resulting in a mesh. - -* [Label Maximum Extension Map](https://clij.github.io/clij2-docs/reference_labelMaximumExtensionMap) -Takes a label map, determines for every label the maximum distance of any pixel to the centroid and replaces every label with the that number. - -* [Label Maximum Extension Ratio Map](https://clij.github.io/clij2-docs/reference_labelMaximumExtensionRatioMap) -Takes a label map, determines for every label the maximum distance of any pixel to the centroid and replaces every label with the that number. - -* [Label Pixel Count Map](https://clij.github.io/clij2-docs/reference_labelPixelCountMap) -Takes a label map, determines the number of pixels per label and replaces every label with the that number. - -* [Label Standard Deviation Intensity Map](https://clij.github.io/clij2-docs/reference_labelStandardDeviationIntensityMap) -Takes an image and a corresponding label map, determines the standard deviation of the intensity per label and replaces every label with the that number. - -* [Local Maximum Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_localMaximumAverageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point - and replaces every label with the maximum distance of touching labels. - -* [Local Maximum Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_localMaximumAverageNeighborDistanceMap) -Takes a label map, determines which labels touch, the distance between their centroids and the maximum distancebetween touching neighbors. It then replaces every label with the that value. - -* [Local Maximum Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_localMaximumTouchingNeighborCountMap) -Takes a label map, determines which labels touch, determines for every label with the number of touching -neighboring labels and replaces the label index with the local maximum of this count. - -* [Local Mean Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_localMeanAverageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point - and replaces every label with the mean distance of touching labels. - -* [Local Mean Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_localMeanAverageNeighborDistanceMap) -Takes a label map, determines which labels touch, the distance between their centroids and the mean distancebetween touching neighbors. It then replaces every label with the that value. - -* [Local Mean Touch Portion Map](https://clij.github.io/clij2-docs/reference_localMeanTouchPortionMap) -Takes a label map, determines which labels touch and how much, relatively taking the whole outline of -each label into account, and determines for every label with the mean of this value and replaces the -label index with that value. - -* [Local Mean Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_localMeanTouchingNeighborCountMap) -Takes a label map, determines which labels touch, determines for every label with the number of touching -neighboring labels and replaces the label index with the local mean of this count. - -* [Local Median Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_localMedianAverageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point - and replaces every label with the median distance of touching labels. - -* [Local Median Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_localMedianAverageNeighborDistanceMap) -Takes a label map, determines which labels touch, the distance between their centroids and the median distancebetween touching neighbors. It then replaces every label with the that value. - -* [Local Median Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_localMedianTouchingNeighborCountMap) -Takes a label map, determines which labels touch, determines for every label with the number of touching -neighboring labels and replaces the label index with the local median of this count. - -* [Local Minimum Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_localMinimumAverageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point - and replaces every label with the minimum distance of touching labels. - -* [Local Minimum Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_localMinimumAverageNeighborDistanceMap) -Takes a label map, determines which labels touch, the distance between their centroids and the minimum distancebetween touching neighbors. It then replaces every label with the that value. - -* [Local Minimum Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_localMinimumTouchingNeighborCountMap) -Takes a label map, determines which labels touch, determines for every label with the number of touching -neighboring labels and replaces the label index with the local minimum of this count. - -* [Local Standard Deviation Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_localStandardDeviationAverageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point - and replaces every label with the standard deviation distance of touching labels. - -* [Local Standard Deviation Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_localStandardDeviationAverageNeighborDistanceMap) -Takes a label map, determines which labels touch, the distance between their centroids and the standard deviation distancebetween touching neighbors. It then replaces every label with the that value. - -* [Local Standard Deviation Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_localStandardDeviationTouchingNeighborCountMap) -Takes a label map, determines which labels touch, determines for every label with the number of touching -neighboring labels and replaces the label index with the local standard deviation of this count. - -* [Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_touchingNeighborCountMap) -Takes a label map, determines which labels touch and replaces every label with the number of touching neighboring labels. - -* [Weka Label Classifier](https://clij.github.io/clij2-docs/reference_wekaLabelClassifier) -Applies a pre-trained CLIJx-Weka model to an image and a corresponding label map. - - - -28 operations listed. - - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) diff --git a/parameter_optimization.md b/parameter_optimization.md deleted file mode 100644 index 1a77512..0000000 --- a/parameter_optimization.md +++ /dev/null @@ -1,40 +0,0 @@ -# Parameter optimization for binarization operations -If a workflow results in a binary image and some annotations are availabe, its parameters can be automatically optimized. -![Image](images/optimize.png) - - -To prepare the optimization, draw Freehand or Polyline ROIs and store them in the ROI Manager. -Name the positive annotations "p" and the negative annotations "n" (background). -If the ROI manager doesn't contain any annotations, it will open and start the annotation tool which is very similar to ImageJs Freehand tool. -Use the mouse to annotate positive regions and they will automatically be saved to the ROI manger with the correct name. -If you hold CTRL while drawing, they will be stored as negative/background annotations in the ROI manager. - -When annotations are ready, click the "Optimize parameters" button: - - -[Download video](images/optimize.mp4) [Image data source: Broad BioImage Benchmark collection](https://bbbc.broadinstitute.org/BBBC008) - -If optimization doesn't work so well, consider using a configurable optimizer: -![Image](images/optimize_parameters_configurable.png) - -It allows you to increase the optimization range of parameters. A range of 6 typically means it will optimize parameters -starting from the large step size (also applied when clicking the `>>` button) times 2 to the power of -3 to 2 to the power of 3: - -![Image](images/optimize_parameters_configurable_range.png) - -Furthermore, you can select which parameters should be optimized in groups (typically X and Y parameters should be grouped) and -which parameters should stay constant: - -![Image](images/optimize_parameters_configurable_variables.png) - -In case the optimization fails and you want to go back, use the [undo](https://clij.github.io/assistant/undo) functionality. - -Last but not least, for good scientific practice, please save the annotations and the protocol of the analysis. -Annotations can be saved using the `More >>` button in the ROI Manager: -![Image](images/optimize_parameters_save_annotations.png) - -The latter can be done by [exporting a script or human readable workflow](https://clij.github.io/assistant/macro_export). - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/readme.md b/readme.md index 6d1d68a..59fd774 100644 --- a/readme.md +++ b/readme.md @@ -1,103 +1,33 @@ # CLIJx-Assistant -[CLIJx-Assistant](https://clij.github.io/assistant) is an intuitive user interface for building custom GPU-accelerated image processing workflows using [CLIJ2](https://clij.github.io) in [Fiji](https://fiji.sc). -It visualizes workflows as image date flow graphs while building them. -It suggests what to do next and generates scripts and human readable protocols to facilitate reproducible bio-image analysis. -These generated scripts also be executed in other platforms such as Matlab, Icy, Python and QuPath. +[CLIJx-Assistant](https://clij.github.io/clijx-assistant) is the eXperiemental sibling of the [CLIJ2-assistant](https://clij.github.io/assistant) with extended functionality under development. If you use CLIJx-assistant, pleace cite it: Robert Haase, Akanksha Jain, Stephane Rigaud, Daniela Vorkel, Pradeep Rajasekhar, Theresa Suckert, Talley J Lambert, Juan Nunez-Iglesias, Daniel P Poole, Pavel Tomancak, Eugene W Myers. Interactive design of GPU-accelerated Image Data Flow Graphs and cross-platform deployment using multi-lingual code generation. [BioRxiv preprint](https://www.biorxiv.org/content/10.1101/2020.11.19.386565v1) -CLIJx-Assistant is under development and is subject to change. -Give it a try and let us know what you think! -Do not use it for routine research yet. -Stay tuned. +CLIJx-Assistant is under development and is subject to change. In case of any issues, please open a thread on [image.sc](https://image.sc) and tag @haesleinhuepf. ![Image](images/teaser_landscape.gif) [Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] ## Overview -* Introduction - * [Installation](https://clij.github.io/assistant/installation) - * [Building workflows](https://clij.github.io/assistant/getting_started) - * [Saving and loading workflows](https://clij.github.io/assistant/save_and_load) - * [Undo parameter changes](https://clij.github.io/assistant/undo) - -* Filtering / correction - * [Image filtering](https://clij.github.io/assistant/filtering) - * [Gamma correction](https://clij.github.io/assistant/gamma_correction) - * [Drift correction](https://clij.github.io/assistant/drift_correction) - -* Transformation - * [Intensity projection](https://clij.github.io/assistant/intensity_projection) - * [Crop, Pan & zoom](https://clij.github.io/assistant/crop_pan_zoom) - * [Cylinder projection](https://clij.github.io/assistant/cylinder_projection) - * [Sphere projection](https://clij.github.io/assistant/sphere_projection) - * [Drift correction](https://clij.github.io/assistant/drift_correction) - -* Regionalisation - * [Nuclei segmentation](https://clij.github.io/assistant/segmentation_nuclei) - * [Cell segmentation based on membranes](https://clij.github.io/assistant/segmentation_cells) - * [Optimize parameters for binarization](https://clij.github.io/assistant/parameter_optimization) - * [Label classification](https://clij.github.io/assistant/clijx_weka_label_classifier) - -* Reproducibility / interoperability - * [Export workflows as ImageJ Script](https://clij.github.io/assistant/macro_export) - * [Export human readable protocols and ImageJ Macro Markdown notebooks](https://clij.github.io/assistant/supplementary_methods_section_generator) - * [Generate CLIJx / Fiji plugins](https://clij.github.io/assistant/generate_clijx_plugins) - * [Export as Icy Protocol](https://clij.github.io/assistant/icy_protocol_export) - * [Export Groovy Script for QuPath](https://clij.github.io/assistant/export_to_clupath) - * [Export workflows as Python script using clEsperanto and Napari](https://clij.github.io/assistant/te_oki_export) - -* [Reference](https://clij.github.io/assistant/reference) - -* Extensibility - * [CLIJ2 Plugin template](https://github.com/clij/clij2-plugin-template) - * [CLIJ2 imglib2 extensions](https://github.com/clij/clijx-assistant-imglib2) - * [CLIJ2 ImageJ extensions](https://github.com/clij/clijx-assistant-imagej) - * [CLIJ2 ImageJ2 extensions](https://github.com/clij/clijx-assistant-imagej2) - * [CLIJ2 BoneJ extensions](https://github.com/clij/clijx-assistant-bonej) - * [CLIJ2 MorphoLibJ extensions](https://github.com/clij/clijx-assistant-morpholibj) - * [CLIJ2 ImageJ 3D Suite extensions](https://github.com/clij/clijx-assistant-imagej3dsuite) - * [CLIJ2 SimpleITK extensions](https://github.com/clij/clijx-assistant-simpleitk) - -## Acknowledgements -We would like to thank everybody who helped developing, testing and motivating this project. In particular thanks go to -Akanksha Jain (EPFL Basel), -Bert Nitzsche (PoL TU Dresden), -Bradley Lowekamp (NIAID Washington), -Bruno C. Vellutini (MPI CBG Dresden), -Christian Tischer (EMBL Heidelberg), -Daniela Vorkel (MPI CBG Dresden), -Eugene W. Myers (MPI CBG Dresden) -Florian Jug (MPI CBG Dresden), -Gayathri Nadar (MPI CBG Dresden), -Irene Seijo Barandiaran (MPI CBG Dresden), -Johannes Girstmair (MPI CBG Dresden), -Juan Nunes-Iglesias (Monash University Melbourne), -Kisha Sivanathan (Harvard Medical School Boston), -Lior Pytowski (University of Oxford), -Marion Louveaux (Institut Pasteur Paris), -Matthias Arzt (MPI CBG Dresden), -Nik Cordes, -Noreen Walker (MPI CBG Dresden), -Pavel Tomancak (MPI CBG Dresden), -Pete Bankhead (University of Edinburgh), -Pradeep Rajasekhar (Monash University Melbourne), -Romain Guiet (EPFL Lausanne), -Sebastian Munck (VIB Leuven), -Stéphane Dallongeville (Institut Pasteur) -Stéphane~Rigaud (Institut Pasteur Paris), -Stein Rørvik, -Talley J. Lambert (Harvard Medical School Boston), -Tanner Fadero (U Chapel Hill), -Theresa Suckert (OncoRay, TU Dresden), - -Furthermore, the constant support by the Image Science and the NEUBIAS communities is fantastic. - -This work was supported by the German Federal Ministry of Research and Education (BMBF) under the code 031L0044 (Sysbio II). - -## Feedback welcome! -I'm eager to receiving feedback: rhaase at mpi minus cbg dot de +* [Installation](https://clij.github.io/assistant/installation#extensions) +* In development + * [Drift correction](https://clij.github.io/clijx-assistant/drift_correction) + * [Intensity projection](https://clij.github.io/clijx-assistant/intensity_projection) + * [Drift correction](https://clij.github.io/clijx-assistant/drift_correction) + * [Label classification](https://clij.github.io/clijx-assistant/clijx_weka_label_classifier) + * [Generate CLIJx / Fiji plugins](https://clij.github.io/clijx-assistant/generate_clijx_plugins) + * [Export Groovy Script for QuPath](https://clij.github.io/clijx-assistant/export_to_clupath) + * [Export workflows as Python script using clEsperanto and Napari](https://clij.github.io/clijx-assistant/te_oki_export) + +* Extensions (in development) + * [Imglib2 extensions](https://github.com/clij/clijx-assistant-imglib2) + * [mageJ extensions](https://github.com/clij/clijx-assistant-imagej) + * [ImageJ2 extensions](https://github.com/clij/clijx-assistant-imagej2) + * [BoneJ extensions](https://github.com/clij/clijx-assistant-bonej) + * [MorphoLibJ extensions](https://github.com/clij/clijx-assistant-morpholibj) + * [ImageJ 3D Suite extensions](https://github.com/clij/clijx-assistant-imagej3dsuite) + * [SimpleITK extensions](https://github.com/clij/clijx-assistant-simpleitk) [Imprint](https://clij.github.io/imprint) diff --git a/reference.md b/reference.md deleted file mode 100644 index 2433fb0..0000000 --- a/reference.md +++ /dev/null @@ -1,809 +0,0 @@ -## CLIIJx-Assistant operations -This is the list of currently supported [CLIJ2](https://clij.github.io/) and [CLIJx](https://clij.github.io/clijx) operations. - -Please note: CLIJx-Assitant is under development. Hence, this list is subject to change. - -* [Absolute Difference](https://clij.github.io/clij2-docs/reference_absoluteDifference) -Determines the absolute difference pixel by pixel between two images. - -* [Absolute](https://clij.github.io/clij2-docs/reference_absolute) -Computes the absolute value of every individual pixel x in a given image. - -* [Add Image And Scalar](https://clij.github.io/clij2-docs/reference_addImageAndScalar) -Adds a scalar value s to all pixels x of a given image X. - -* [Add Images Weighted](https://clij.github.io/clij2-docs/reference_addImagesWeighted) -Calculates the sum of pairs of pixels x and y from images X and Y weighted with factors a and b. - -* [Add Images](https://clij.github.io/clij2-docs/reference_addImages) -Calculates the sum of pairs of pixels x and y of two images X and Y. - -* [Affine Transform2D](https://clij.github.io/clij2-docs/reference_affineTransform2D) -Applies an affine transform to a 2D image. - -* [Affine Transform3D](https://clij.github.io/clij2-docs/reference_affineTransform3D) -Applies an affine transform to a 3D image. - -* [Affine Transform](https://clij.github.io/clij2-docs/reference_affineTransform) -Applies an affine transform to a 2D or 3D image - -* [Apply Vector Field2D](https://clij.github.io/clij2-docs/reference_applyVectorField2D) -Deforms an image according to distances provided in the given vector images. - -* [Apply Vector Field3D](https://clij.github.io/clij2-docs/reference_applyVectorField3D) -Deforms an image stack according to distances provided in the given vector image stacks. - -* [Automatic Threshold](https://clij.github.io/clij2-docs/reference_automaticThreshold) -The automatic thresholder utilizes the threshold methods from ImageJ on a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_averageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids and replaces every label with the average distance to the n closest neighboring labels. - -* [Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_averageNeighborDistanceMap) -Takes a label map, determines which labels touch and replaces every label with the average distance to their neighboring labels. - -* [Bilateral](https://clij.github.io/clij2-docs/reference_bilateral) -Applies a bilateral filter using a box neighborhood with sigma weights for space and intensity to the input image. - -* [Binary And](https://clij.github.io/clij2-docs/reference_binaryAnd) -Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of -pixels x and y with the binary AND operator &. -All pixel values except 0 in the input images are interpreted as 1. - -* [Binary Edge Detection](https://clij.github.io/clij2-docs/reference_binaryEdgeDetection) -Determines pixels/voxels which are on the surface of binary objects and sets only them to 1 in the -destination image. All other pixels are set to 0. - -* [Binary Intersection](https://clij.github.io/clij2-docs/reference_binaryIntersection) -Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of -pixels x and y with the binary intersection operator &. -All pixel values except 0 in the input images are interpreted as 1. - -* [Binary Not](https://clij.github.io/clij2-docs/reference_binaryNot) -Computes a binary image (containing pixel values 0 and 1) from an image X by negating its pixel values -x using the binary NOT operator ! - -* [Binary Or](https://clij.github.io/clij2-docs/reference_binaryOr) -Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of -pixels x and y with the binary OR operator |. - -* [Binary Subtract](https://clij.github.io/clij2-docs/reference_binarySubtract) -Subtracts one binary image from another. - -* [Binary Union](https://clij.github.io/clij2-docs/reference_binaryUnion) -Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of -pixels x and y with the binary union operator |. - -* [Binary Weka Pixel Classifier](https://clij.github.io/clij2-docs/reference_binaryWekaPixelClassifier) -Applies a pre-trained CLIJx-Weka model to a 2D image. - -* [Binary XOr](https://clij.github.io/clij2-docs/reference_binaryXOr) -Computes a binary image (containing pixel values 0 and 1) from two images X and Y by connecting pairs of -pixels x and y with the binary operators AND &, OR | and NOT ! implementing the XOR operator. - -* [Bottom Hat Box](https://clij.github.io/clij2-docs/reference_bottomHatBox) -Apply a bottom-hat filter for background subtraction to the input image. - -* [Bottom Hat Sphere](https://clij.github.io/clij2-docs/reference_bottomHatSphere) -Applies a bottom-hat filter for background subtraction to the input image. - -* [Close Index Gaps In Label Map](https://clij.github.io/clij2-docs/reference_closeIndexGapsInLabelMap) -Analyses a label map and if there are gaps in the indexing (e.g. label 5 is not present) all -subsequent labels will be relabelled. - -* [Closing Box](https://clij.github.io/clij2-docs/reference_closingBox) -Apply a binary closing to the input image by calling n dilations and n erosions subsequenntly. - -* [Closing Diamond](https://clij.github.io/clij2-docs/reference_closingDiamond) -Apply a binary closing to the input image by calling n dilations and n erosions subsequently. - -* [Combine Horizontally](https://clij.github.io/clij2-docs/reference_combineHorizontally) -Combines two images or stacks in X. - -* [Combine Vertically](https://clij.github.io/clij2-docs/reference_combineVertically) -Combines two images or stacks in Y. - -* [Concatenate Stacks](https://clij.github.io/clij2-docs/reference_concatenateStacks) -Concatenates two stacks in Z. - -* [Connected Components Labeling Box](https://clij.github.io/clij2-docs/reference_connectedComponentsLabelingBox) -Performs connected components analysis inspecting the box neighborhood of every pixel to a binary image and generates a label map. - -* [Connected Components Labeling Diamond](https://clij.github.io/clij2-docs/reference_connectedComponentsLabelingDiamond) -Performs connected components analysis inspecting the diamond neighborhood of every pixel to a binary image and generates a label map. - -* [Convolve](https://clij.github.io/clij2-docs/reference_convolve) -Convolve the image with a given kernel image. - -* [Copy Slice](https://clij.github.io/clij2-docs/reference_copySlice) -This method has two purposes: -It copies a 2D image to a given slice z position in a 3D image stack or -It copies a given slice at position z in an image stack to a 2D image. - -* [Copy](https://clij.github.io/clij2-docs/reference_copy) -Copies an image. - -* [Count Non Zero Pixels Slice By Slice Sphere](https://clij.github.io/clij2-docs/reference_countNonZeroPixelsSliceBySliceSphere) -Counts non-zero pixels in a sphere around every pixel slice by slice in a stack. - -* [Count Non Zero Pixels2D Sphere](https://clij.github.io/clij2-docs/reference_countNonZeroPixels2DSphere) -Counts non-zero pixels in a sphere around every pixel. - -* [Count Non Zero Voxels3D Sphere](https://clij.github.io/clij2-docs/reference_countNonZeroVoxels3DSphere) -Counts non-zero voxels in a sphere around every voxel. - -* [Crop2D](https://clij.github.io/clij2-docs/reference_crop2D) -Crops a given rectangle out of a given image. - -* [Crop3D](https://clij.github.io/clij2-docs/reference_crop3D) -Crops a given sub-stack out of a given image stack. - -* [Cylinder Transform](https://clij.github.io/clij2-docs/reference_cylinderTransform) -Applies a cylinder transform to an image stack assuming the center line goes in Y direction in the center of the stack. - -* [Detect And Label Maxima](https://clij.github.io/clij2-docs/reference_detectAndLabelMaxima) -Determines maximum regions in a Gaussian blurred version of the original image. - -* [Detect Label Edges](https://clij.github.io/clij2-docs/reference_detectLabelEdges) -Takes a labelmap and returns an image where all pixels on label edges are set to 1 and all other pixels to 0. - -* [Detect Maxima2D Box](https://clij.github.io/clij2-docs/reference_detectMaxima2DBox) -Detects local maxima in a given square/cubic neighborhood. - -* [Detect Maxima3D Box](https://clij.github.io/clij2-docs/reference_detectMaxima3DBox) -Detects local maxima in a given square/cubic neighborhood. - -* [Detect Minima Box](https://clij.github.io/clij2-docs/reference_detectMinimaBox) -Detects local minima in a given square/cubic neighborhood. - -* [Detect Minima2D Box](https://clij.github.io/clij2-docs/reference_detectMinima2DBox) -Detects local minima in a given square/cubic neighborhood. - -* [Detect Minima3D Box](https://clij.github.io/clij2-docs/reference_detectMinima3DBox) -Detects local minima in a given square/cubic neighborhood. - -* [Difference Of Gaussian2D](https://clij.github.io/clij2-docs/reference_differenceOfGaussian2D) -Applies Gaussian blur to the input image twice with different sigma values resulting in two images which are then subtracted from each other. - -* [Difference Of Gaussian3D](https://clij.github.io/clij2-docs/reference_differenceOfGaussian3D) -Applies Gaussian blur to the input image twice with different sigma values resulting in two images which are then subtracted from each other. - -* [Dilate Box](https://clij.github.io/clij2-docs/reference_dilateBox) -Computes a binary image with pixel values 0 and 1 containing the binary dilation of a given input image. - -* [Dilate Sphere](https://clij.github.io/clij2-docs/reference_dilateSphere) -Computes a binary image with pixel values 0 and 1 containing the binary dilation of a given input image. - -* [Divide By Gaussian Background](https://clij.github.io/clij2-docs/reference_divideByGaussianBackground) -Applies Gaussian blur to the input image and divides the original by the result. - -* [Divide Images](https://clij.github.io/clij2-docs/reference_divideImages) -Divides two images X and Y by each other pixel wise. - -* [Draw Distance Mesh Between Touching Labels](https://clij.github.io/clij2-docs/reference_drawDistanceMeshBetweenTouchingLabels) -Starting from a label map, draw lines between touching neighbors resulting in a mesh. - -* [Draw Mesh Between N Closest Labels](https://clij.github.io/clij2-docs/reference_drawMeshBetweenNClosestLabels) -Starting from a label map, draw lines between n closest labels for each label resulting in a mesh. - -* [Draw Mesh Between Proximal Labels](https://clij.github.io/clij2-docs/reference_drawMeshBetweenProximalLabels) -Starting from a label map, draw lines between labels that are closer than a given distance resulting in a mesh. - -* [Draw Mesh Between Touching Labels](https://clij.github.io/clij2-docs/reference_drawMeshBetweenTouchingLabels) -Starting from a label map, draw lines between touching neighbors resulting in a mesh. - -* [Draw Touch Count Mesh Between Touching Labels](https://clij.github.io/clij2-docs/reference_drawTouchCountMeshBetweenTouchingLabels) -Starting from a label map, draw lines between touching neighbors resulting in a mesh. - -* [Draw Touch Portion Mesh Between Touching Labels](https://clij.github.io/clij2-docs/reference_drawTouchPortionMeshBetweenTouchingLabels) -Starting from a label map, draw lines between touching neighbors resulting in a mesh. - -* [Drift Correction By Center Of Mass Fixation](https://clij.github.io/clij2-docs/reference_driftCorrectionByCenterOfMassFixation) -Determines the centerOfMass of the image stack and translates it so that it stays in a defined position. - -* [Drift Correction By Centroid Fixation](https://clij.github.io/clij2-docs/reference_driftCorrectionByCentroidFixation) -Threshold the image stack, determines the centroid of the resulting binary image and -translates the image stack so that its centroid sits in a defined position. - -* [Entropy Box](https://clij.github.io/clij2-docs/reference_entropyBox) -Determines the local entropy in a box with a given radius around every pixel. - -* [Equal Constant](https://clij.github.io/clij2-docs/reference_equalConstant) -Determines if an image A and a constant b are equal. - -* [Equal](https://clij.github.io/clij2-docs/reference_equal) -Determines if two images A and B equal pixel wise. - -* [Equalize Mean Intensities Of Slices](https://clij.github.io/clij2-docs/reference_equalizeMeanIntensitiesOfSlices) -Determines correction factors for each z-slice so that the average intensity in all slices can be made the same and multiplies these factors with the slices. - -* [Erode Box](https://clij.github.io/clij2-docs/reference_erodeBox) -Computes a binary image with pixel values 0 and 1 containing the binary erosion of a given input image. - -* [Erode Sphere](https://clij.github.io/clij2-docs/reference_erodeSphere) -Computes a binary image with pixel values 0 and 1 containing the binary erosion of a given input image. - -* [Euclidean Distance From Label Centroid Map](https://clij.github.io/clij2-docs/reference_euclideanDistanceFromLabelCentroidMap) -Takes a label map, determines the centroids of all labels and writes the distance of all labelled pixels to their centroid in the result image. -Background pixels stay zero. - -* [Exclude Labels On Edges](https://clij.github.io/clij2-docs/reference_excludeLabelsOnEdges) -Removes all labels from a label map which touch the edges of the image (in X, Y and Z if the image is 3D). - -* [Exclude Labels Outside Size Range](https://clij.github.io/clij2-docs/reference_excludeLabelsOutsideSizeRange) -Removes labels from a label map which are not within a certain size range. - -* [Exclude Labels With Values Out Of Range](https://clij.github.io/clij2-docs/reference_excludeLabelsWithValuesOutOfRange) -This operation removes labels from a labelmap and renumbers the remaining labels. - -* [Exclude Labels With Values Within Range](https://clij.github.io/clij2-docs/reference_excludeLabelsWithValuesWithinRange) -This operation removes labels from a labelmap and renumbers the remaining labels. - -* [Exponential](https://clij.github.io/clij2-docs/reference_exponential) -Computes base exponential of all pixels values. - -* [Extend Labeling Via Voronoi](https://clij.github.io/clij2-docs/reference_extendLabelingViaVoronoi) -Takes a label map image and dilates the regions using a octagon shape until they touch. - -* [Extend Labels With Maximum Radius](https://clij.github.io/clij2-docs/reference_extendLabelsWithMaximumRadius) -Extend labels with a given radius. - -* [Extended Depth Of Focus Sobel Projection](https://clij.github.io/clij2-docs/reference_extendedDepthOfFocusSobelProjection) -Extended depth of focus projection maximizing intensity in the local sobel image. - -* [Extended Depth Of Focus Tenengrad Projection](https://clij.github.io/clij2-docs/reference_extendedDepthOfFocusTenengradProjection) -Extended depth of focus projection maximizing intensity in the local sobel image. - -* [Extended Depth Of Focus Variance Projection](https://clij.github.io/clij2-docs/reference_extendedDepthOfFocusVarianceProjection) -Extended depth of focus projection maximizing local pixel intensity variance. - -* [Find Maxima Plateaus](https://clij.github.io/clij2-docs/reference_findMaximaPlateaus) -Finds local maxima, which might be groups of pixels with the same intensity and marks them in a binary image. - -* [Flip2D](https://clij.github.io/clij2-docs/reference_flip2D) -Flips an image in X and/or Y direction depending on boolean flags. - -* [Flip3D](https://clij.github.io/clij2-docs/reference_flip3D) -Flips an image in X, Y and/or Z direction depending on boolean flags. - -* [Gamma Correction](https://clij.github.io/clij2-docs/reference_gammaCorrection) -Applies a gamma correction to an image. - -* [Gaussian Blur2D](https://clij.github.io/clij2-docs/reference_gaussianBlur2D) -Computes the Gaussian blurred image of an image given two sigma values in X and Y. - -* [Gaussian Blur3D](https://clij.github.io/clij2-docs/reference_gaussianBlur3D) -Computes the Gaussian blurred image of an image given two sigma values in X, Y and Z. - -* [Gradient X](https://clij.github.io/clij2-docs/reference_gradientX) -Computes the gradient of gray values along X. - -* [Gradient Y](https://clij.github.io/clij2-docs/reference_gradientY) -Computes the gradient of gray values along Y. - -* [Gradient Z](https://clij.github.io/clij2-docs/reference_gradientZ) -Computes the gradient of gray values along Z. - -* [Greater Constant](https://clij.github.io/clij2-docs/reference_greaterConstant) -Determines if two images A and B greater pixel wise. - -* [Greater Or Equal Constant](https://clij.github.io/clij2-docs/reference_greaterOrEqualConstant) -Determines if two images A and B greater or equal pixel wise. - -* [Greater Or Equal](https://clij.github.io/clij2-docs/reference_greaterOrEqual) -Determines if two images A and B greater or equal pixel wise. - -* [Greater](https://clij.github.io/clij2-docs/reference_greater) -Determines if two images A and B greater pixel wise. - -* [Image To Stack](https://clij.github.io/clij2-docs/reference_imageToStack) -Copies a single slice into a stack a given number of times. - -* [Intensity Correction Above Threshold Otsu](https://clij.github.io/clij2-docs/reference_intensityCorrectionAboveThresholdOtsu) -Determines the mean intensity of all pixel the image stack which are above a determined Threshold (Otsu et al. 1979) and multiplies it with a factor so that the mean intensity becomes equal to a given value. - -* [Intensity Correction](https://clij.github.io/clij2-docs/reference_intensityCorrection) -Determines the mean intensity of the image stack and multiplies it with a factor so that the mean intensity becomes equal to a given value. - -* [Invert](https://clij.github.io/clij2-docs/reference_invert) -Computes the negative value of all pixels in a given image. - -* [Label Maximum Extension Map](https://clij.github.io/clij2-docs/reference_labelMaximumExtensionMap) -Takes a label map, determines for every label the maximum distance of any pixel to the centroid and replaces every label with the that number. - -* [Label Maximum Extension Ratio Map](https://clij.github.io/clij2-docs/reference_labelMaximumExtensionRatioMap) -Takes a label map, determines for every label the maximum distance of any pixel to the centroid and replaces every label with the that number. - -* [Label Maximum Intensity Map](https://clij.github.io/clij2-docs/reference_labelMaximumIntensityMap) -Takes an image and a corresponding label map, determines the mean intensity per label and replaces every label with the that number. - -* [Label Mean Extension Map](https://clij.github.io/clij2-docs/reference_labelMeanExtensionMap) -Takes a label map, determines for every label the mean distance of any pixel to the centroid and replaces every label with the that number. - -* [Label Mean Intensity Map](https://clij.github.io/clij2-docs/reference_labelMeanIntensityMap) -Takes an image and a corresponding label map, determines the mean intensity per label and replaces every label with the that number. - -* [Label Minimum Intensity Map](https://clij.github.io/clij2-docs/reference_labelMinimumIntensityMap) -Takes an image and a corresponding label map, determines the mean intensity per label and replaces every label with the that number. - -* [Label Pixel Count Map](https://clij.github.io/clij2-docs/reference_labelPixelCountMap) -Takes a label map, determines the number of pixels per label and replaces every label with the that number. - -* [Label Spots](https://clij.github.io/clij2-docs/reference_labelSpots) -Transforms a binary image with single pixles set to 1 to a labelled spots image. - -* [Label Standard Deviation Intensity Map](https://clij.github.io/clij2-docs/reference_labelStandardDeviationIntensityMap) -Takes an image and a corresponding label map, determines the standard deviation of the intensity per label and replaces every label with the that number. - -* [Label Surface](https://clij.github.io/clij2-docs/reference_labelSurface) -Takes a label map and excludes all labels which are not on the surface. - -* [Label To Mask](https://clij.github.io/clij2-docs/reference_labelToMask) -Masks a single label in a label map. - -* [Label Voronoi Octagon](https://clij.github.io/clij2-docs/reference_labelVoronoiOctagon) -Takes a labelled image and dilates the labels using a octagon shape until they touch. - -* [Labeling Workflow A L X](https://clij.github.io/clij2-docs/reference_labelingWorkflowALX) -A segmentation workflow using maxima detection, thresholding, maximum filters and label edge detection. - -* [Laplace Box](https://clij.github.io/clij2-docs/reference_laplaceBox) -Applies the Laplace operator (Box neighborhood) to an image. - -* [Laplacian Of Gaussian3D](https://clij.github.io/clij2-docs/reference_laplacianOfGaussian3D) -Determined the Laplacian of Gaussian of a give input image with a given sigma. - -* [Local Maximum Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_localMaximumAverageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point - and replaces every label with the maximum distance of touching labels. - -* [Local Maximum Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_localMaximumAverageNeighborDistanceMap) -Takes a label map, determines which labels touch, the distance between their centroids and the maximum distancebetween touching neighbors. It then replaces every label with the that value. - -* [Local Maximum Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_localMaximumTouchingNeighborCountMap) -Takes a label map, determines which labels touch, determines for every label with the number of touching -neighboring labels and replaces the label index with the local maximum of this count. - -* [Local Mean Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_localMeanAverageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point - and replaces every label with the mean distance of touching labels. - -* [Local Mean Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_localMeanAverageNeighborDistanceMap) -Takes a label map, determines which labels touch, the distance between their centroids and the mean distancebetween touching neighbors. It then replaces every label with the that value. - -* [Local Mean Touch Portion Map](https://clij.github.io/clij2-docs/reference_localMeanTouchPortionMap) -Takes a label map, determines which labels touch and how much, relatively taking the whole outline of -each label into account, and determines for every label with the mean of this value and replaces the -label index with that value. - -* [Local Mean Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_localMeanTouchingNeighborCountMap) -Takes a label map, determines which labels touch, determines for every label with the number of touching -neighboring labels and replaces the label index with the local mean of this count. - -* [Local Median Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_localMedianAverageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point - and replaces every label with the median distance of touching labels. - -* [Local Median Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_localMedianAverageNeighborDistanceMap) -Takes a label map, determines which labels touch, the distance between their centroids and the median distancebetween touching neighbors. It then replaces every label with the that value. - -* [Local Median Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_localMedianTouchingNeighborCountMap) -Takes a label map, determines which labels touch, determines for every label with the number of touching -neighboring labels and replaces the label index with the local median of this count. - -* [Local Minimum Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_localMinimumAverageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point - and replaces every label with the minimum distance of touching labels. - -* [Local Minimum Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_localMinimumAverageNeighborDistanceMap) -Takes a label map, determines which labels touch, the distance between their centroids and the minimum distancebetween touching neighbors. It then replaces every label with the that value. - -* [Local Minimum Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_localMinimumTouchingNeighborCountMap) -Takes a label map, determines which labels touch, determines for every label with the number of touching -neighboring labels and replaces the label index with the local minimum of this count. - -* [Local Standard Deviation Average Distance Of N Closest Neighbors Map](https://clij.github.io/clij2-docs/reference_localStandardDeviationAverageDistanceOfNClosestNeighborsMap) -Takes a label map, determines distances between all centroids, the mean distance of the n closest points for every point - and replaces every label with the standard deviation distance of touching labels. - -* [Local Standard Deviation Average Neighbor Distance Map](https://clij.github.io/clij2-docs/reference_localStandardDeviationAverageNeighborDistanceMap) -Takes a label map, determines which labels touch, the distance between their centroids and the standard deviation distancebetween touching neighbors. It then replaces every label with the that value. - -* [Local Standard Deviation Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_localStandardDeviationTouchingNeighborCountMap) -Takes a label map, determines which labels touch, determines for every label with the number of touching -neighboring labels and replaces the label index with the local standard deviation of this count. - -* [Local Threshold](https://clij.github.io/clij2-docs/reference_localThreshold) -Computes a binary image with pixel values 0 and 1 depending on if a pixel value x in image X -was above of equal to the pixel value m in mask image M. - -* [Logarithm](https://clij.github.io/clij2-docs/reference_logarithm) -Computes base e logarithm of all pixels values. - -* [Make Isotropic](https://clij.github.io/clij2-docs/reference_makeIsotropic) -Applies a scaling operation using linear interpolation to generate an image stack with a given isotropic voxel size. - -* [Mask Label](https://clij.github.io/clij2-docs/reference_maskLabel) -Computes a masked image by applying a label mask to an image. - -* [Mask Stack With Plane](https://clij.github.io/clij2-docs/reference_maskStackWithPlane) -Computes a masked image by applying a binary 2D mask to an image stack. - -* [Mask](https://clij.github.io/clij2-docs/reference_mask) -Computes a masked image by applying a binary mask to an image. - -* [Maximum Image And Scalar](https://clij.github.io/clij2-docs/reference_maximumImageAndScalar) -Computes the maximum of a constant scalar s and each pixel value x in a given image X. - -* [Maximum Images](https://clij.github.io/clij2-docs/reference_maximumImages) -Computes the maximum of a pair of pixel values x, y from two given images X and Y. - -* [Maximum Octagon](https://clij.github.io/clij2-docs/reference_maximumOctagon) -Applies a maximum filter with kernel size 3x3 n times to an image iteratively. - -* [Maximum X Projection](https://clij.github.io/clij2-docs/reference_maximumXProjection) -Determines the maximum intensity projection of an image along X. - -* [Maximum Y Projection](https://clij.github.io/clij2-docs/reference_maximumYProjection) -Determines the maximum intensity projection of an image along X. - -* [Maximum Z Projection Bounded](https://clij.github.io/clij2-docs/reference_maximumZProjectionBounded) -Determines the maximum intensity projection of an image along Z within a given z range. - -* [Maximum Z Projection](https://clij.github.io/clij2-docs/reference_maximumZProjection) -Determines the maximum intensity projection of an image along Z. - -* [Maximum2D Box](https://clij.github.io/clij2-docs/reference_maximum2DBox) -Computes the local maximum of a pixels rectangular neighborhood. - -* [Maximum2D Sphere](https://clij.github.io/clij2-docs/reference_maximum2DSphere) -Computes the local maximum of a pixels ellipsoidal neighborhood. - -* [Maximum3D Box](https://clij.github.io/clij2-docs/reference_maximum3DBox) -Computes the local maximum of a pixels cube neighborhood. - -* [Maximum3D Sphere](https://clij.github.io/clij2-docs/reference_maximum3DSphere) -Computes the local maximum of a pixels spherical neighborhood. - -* [Mean X Projection](https://clij.github.io/clij2-docs/reference_meanXProjection) -Determines the mean average intensity projection of an image along X. - -* [Mean Y Projection](https://clij.github.io/clij2-docs/reference_meanYProjection) -Determines the mean average intensity projection of an image along Y. - -* [Mean Z Projection Above Threshold](https://clij.github.io/clij2-docs/reference_meanZProjectionAboveThreshold) -Determines the mean average intensity projection of an image along Z but only for pixels above a given threshold. - -* [Mean Z Projection Below Threshold](https://clij.github.io/clij2-docs/reference_meanZProjectionBelowThreshold) -Determines the mean average intensity projection of an image along Z but only for pixels below a given threshold. - -* [Mean Z Projection Bounded](https://clij.github.io/clij2-docs/reference_meanZProjectionBounded) -Determines the mean average intensity projection of an image along Z within a given z range. - -* [Mean Z Projection](https://clij.github.io/clij2-docs/reference_meanZProjection) -Determines the mean average intensity projection of an image along Z. - -* [Mean2D Box](https://clij.github.io/clij2-docs/reference_mean2DBox) -Computes the local mean average of a pixels rectangular neighborhood. - -* [Mean2D Sphere](https://clij.github.io/clij2-docs/reference_mean2DSphere) -Computes the local mean average of a pixels ellipsoidal neighborhood. - -* [Mean3D Box](https://clij.github.io/clij2-docs/reference_mean3DBox) -Computes the local mean average of a pixels cube neighborhood. - -* [Mean3D Sphere](https://clij.github.io/clij2-docs/reference_mean3DSphere) -Computes the local mean average of a pixels spherical neighborhood. - -* [Median Z Projection](https://clij.github.io/clij2-docs/reference_medianZProjection) -Determines the median intensity projection of an image stack along Z. - -* [Median3D Box](https://clij.github.io/clij2-docs/reference_median3DBox) -Computes the local median of a pixels cuboid neighborhood. - -* [Merge Touching Labels](https://clij.github.io/clij2-docs/reference_mergeTouchingLabels) - - -* [Minimum Image And Scalar](https://clij.github.io/clij2-docs/reference_minimumImageAndScalar) -Computes the minimum of a constant scalar s and each pixel value x in a given image X. - -* [Minimum Images](https://clij.github.io/clij2-docs/reference_minimumImages) -Computes the minimum of a pair of pixel values x, y from two given images X and Y. - -* [Minimum Octagon](https://clij.github.io/clij2-docs/reference_minimumOctagon) -Applies a minimum filter with kernel size 3x3 n times to an image iteratively. - -* [Minimum X Projection](https://clij.github.io/clij2-docs/reference_minimumXProjection) -Determines the minimum intensity projection of an image along Y. - -* [Minimum Y Projection](https://clij.github.io/clij2-docs/reference_minimumYProjection) -Determines the minimum intensity projection of an image along Y. - -* [Minimum Z Projection Bounded](https://clij.github.io/clij2-docs/reference_minimumZProjectionBounded) -Determines the minimum intensity projection of an image along Z within a given z range. - -* [Minimum Z Projection Thresholded Bounded](https://clij.github.io/clij2-docs/reference_minimumZProjectionThresholdedBounded) -Determines the minimum intensity projection of all pixels in an image above a given threshold along Z within a given z range. - -* [Minimum Z Projection](https://clij.github.io/clij2-docs/reference_minimumZProjection) -Determines the minimum intensity projection of an image along Z. - -* [Minimum2D Box](https://clij.github.io/clij2-docs/reference_minimum2DBox) -Computes the local minimum of a pixels rectangular neighborhood. - -* [Minimum2D Sphere](https://clij.github.io/clij2-docs/reference_minimum2DSphere) -Computes the local minimum of a pixels ellipsoidal neighborhood. - -* [Minimum3D Box](https://clij.github.io/clij2-docs/reference_minimum3DBox) -Computes the local minimum of a pixels cube neighborhood. - -* [Minimum3D Sphere](https://clij.github.io/clij2-docs/reference_minimum3DSphere) -Computes the local minimum of a pixels spherical neighborhood. - -* [Multiply Image And Scalar](https://clij.github.io/clij2-docs/reference_multiplyImageAndScalar) -Multiplies all pixels value x in a given image X with a constant scalar s. - -* [Multiply Images](https://clij.github.io/clij2-docs/reference_multiplyImages) -Multiplies all pairs of pixel values x and y from two image X and Y. - -* [Multiply Stack With Plane](https://clij.github.io/clij2-docs/reference_multiplyStackWithPlane) -Multiplies all pairs of pixel values x and y from an image stack X and a 2D image Y. - -* [Non Local Means](https://clij.github.io/clij2-docs/reference_nonLocalMeans) -Applies a non-local means filter using a box neighborhood with a Gaussian weight specified with sigma to the input image. - -* [Not Equal Constant](https://clij.github.io/clij2-docs/reference_notEqualConstant) -Determines if two images A and B equal pixel wise. - -* [Not Equal](https://clij.github.io/clij2-docs/reference_notEqual) -Determines if two images A and B equal pixel wise. - -* [Opening Box](https://clij.github.io/clij2-docs/reference_openingBox) -Apply a binary opening to the input image by calling n erosions and n dilations subsequenntly. - -* [Parametric Watershed](https://clij.github.io/clij2-docs/reference_parametricWatershed) -Apply a binary watershed to a binary image and introduce black pixels between objects. - -* [Power Images](https://clij.github.io/clij2-docs/reference_powerImages) -Calculates x to the power of y pixel wise of two images X and Y. - -* [Power](https://clij.github.io/clij2-docs/reference_power) -Computes all pixels value x to the power of a given exponent a. - -* [Pull To ROIManager](https://clij.github.io/clij2-docs/reference_pullToROIManager) -Pulls a binary image from the GPU memory and puts it in the ROI Manager. - -* [Reduce Labels To Labelled Spots](https://clij.github.io/clij2-docs/reference_reduceLabelsToLabelledSpots) -Takes a label map and reduces all labels to their center spots. Label IDs stay and background will be zero. - -* [Reduce Stack](https://clij.github.io/clij2-docs/reference_reduceStack) -Reduces the number of slices in a stack by a given factor. -With the offset you have control which slices stay: -* With factor 3 and offset 0, slices 0, 3, 6,... are kept. * With factor 4 and offset 1, slices 1, 5, 9,... are kept. - -* [Replace Pixels If Zero](https://clij.github.io/clij2-docs/reference_replacePixelsIfZero) -Replaces pixel values x with y in case x is zero. - -* [Reslice Bottom](https://clij.github.io/clij2-docs/reference_resliceBottom) -Flippes Y and Z axis of an image stack. This operation is similar to ImageJs 'Reslice [/]' method but -offers less flexibility such as interpolation. - -* [Reslice Left](https://clij.github.io/clij2-docs/reference_resliceLeft) -Flippes X, Y and Z axis of an image stack. This operation is similar to ImageJs 'Reslice [/]' method - but offers less flexibility such as interpolation. - -* [Reslice Right](https://clij.github.io/clij2-docs/reference_resliceRight) -Flippes X, Y and Z axis of an image stack. This operation is similar to ImageJs 'Reslice [/]' method - but offers less flexibility such as interpolation. - -* [Reslice Top](https://clij.github.io/clij2-docs/reference_resliceTop) -Flippes Y and Z axis of an image stack. This operation is similar to ImageJs 'Reslice [/]' method but -offers less flexibility such as interpolation. - -* [Rigid Transform](https://clij.github.io/clij2-docs/reference_rigidTransform) -Applies a rigid transform using linear interpolation to an image stack. - -* [Rotate Clockwise](https://clij.github.io/clij2-docs/reference_rotateClockwise) -Rotates a given input image by 90 degrees clockwise. - -* [Rotate Counter Clockwise](https://clij.github.io/clij2-docs/reference_rotateCounterClockwise) -Rotates a given input image by 90 degrees counter-clockwise. - -* [Rotate2D](https://clij.github.io/clij2-docs/reference_rotate2D) -Rotates an image in plane. - -* [Rotate3D](https://clij.github.io/clij2-docs/reference_rotate3D) -Rotates an image stack in 3D. - -* [Smaller Constant](https://clij.github.io/clij2-docs/reference_smallerConstant) -Determines if two images A and B smaller pixel wise. - -* [Smaller Or Equal Constant](https://clij.github.io/clij2-docs/reference_smallerOrEqualConstant) -Determines if two images A and B smaller or equal pixel wise. - -* [Smaller Or Equal](https://clij.github.io/clij2-docs/reference_smallerOrEqual) -Determines if two images A and B smaller or equal pixel wise. - -* [Smaller](https://clij.github.io/clij2-docs/reference_smaller) -Determines if two images A and B smaller pixel wise. - -* [Sobel Slice By Slice](https://clij.github.io/clij2-docs/reference_sobelSliceBySlice) -Convolve the image with the Sobel kernel slice by slice. - -* [Sobel](https://clij.github.io/clij2-docs/reference_sobel) -Convolve the image with the Sobel kernel. - -* [Sphere Transform](https://clij.github.io/clij2-docs/reference_sphereTransform) -Turns an image stack in XYZ cartesian coordinate system to an AID polar coordinate system. - -* [Squared Difference](https://clij.github.io/clij2-docs/reference_squaredDifference) -Determines the squared difference pixel by pixel between two images. - -* [Standard Deviation Box](https://clij.github.io/clij2-docs/reference_standardDeviationBox) -Computes the local standard deviation of a pixels box neighborhood. - -* [Standard Deviation Sphere](https://clij.github.io/clij2-docs/reference_standardDeviationSphere) -Computes the local standard deviation of a pixels spherical neighborhood. - -* [Standard Deviation Z Projection](https://clij.github.io/clij2-docs/reference_standardDeviationZProjection) -Determines the standard deviation intensity projection of an image stack along Z. - -* [Subtract Gaussian Background](https://clij.github.io/clij2-docs/reference_subtractGaussianBackground) -Applies Gaussian blur to the input image and subtracts the result from the original image. - -* [Subtract Image From Scalar](https://clij.github.io/clij2-docs/reference_subtractImageFromScalar) -Subtracts one image X from a scalar s pixel wise. - -* [Subtract Images](https://clij.github.io/clij2-docs/reference_subtractImages) -Subtracts one image X from another image Y pixel wise. - -* [Sum X Projection](https://clij.github.io/clij2-docs/reference_sumXProjection) -Determines the sum intensity projection of an image along Z. - -* [Sum Y Projection](https://clij.github.io/clij2-docs/reference_sumYProjection) -Determines the sum intensity projection of an image along Z. - -* [Sum Z Projection](https://clij.github.io/clij2-docs/reference_sumZProjection) -Determines the sum intensity projection of an image along Z. - -* [Tenengrad Slice By Slice](https://clij.github.io/clij2-docs/reference_tenengradSliceBySlice) -Convolve the image with the Tenengrad kernel slice by slice. - -* [Tenengrad](https://clij.github.io/clij2-docs/reference_tenengrad) -Convolve the image with the Tenengrad kernel slice by slice. - -* [Threshold Default](https://clij.github.io/clij2-docs/reference_thresholdDefault) -The automatic thresholder utilizes the Default threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold DoG](https://clij.github.io/clij2-docs/reference_thresholdDoG) -Applies a Difference-of-Gaussian filter to an image and thresholds it with given sigma and threshold values. - -* [Threshold Huang](https://clij.github.io/clij2-docs/reference_thresholdHuang) -The automatic thresholder utilizes the Huang threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold IJ Iso Data](https://clij.github.io/clij2-docs/reference_thresholdIJ_IsoData) -The automatic thresholder utilizes the IJ_IsoData threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Intermodes](https://clij.github.io/clij2-docs/reference_thresholdIntermodes) -The automatic thresholder utilizes the Intermodes threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Iso Data](https://clij.github.io/clij2-docs/reference_thresholdIsoData) -The automatic thresholder utilizes the IsoData threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Li](https://clij.github.io/clij2-docs/reference_thresholdLi) -The automatic thresholder utilizes the Li threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Max Entropy](https://clij.github.io/clij2-docs/reference_thresholdMaxEntropy) -The automatic thresholder utilizes the MaxEntropy threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Mean](https://clij.github.io/clij2-docs/reference_thresholdMean) -The automatic thresholder utilizes the Mean threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Min Error](https://clij.github.io/clij2-docs/reference_thresholdMinError) -The automatic thresholder utilizes the MinError threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Minimum](https://clij.github.io/clij2-docs/reference_thresholdMinimum) -The automatic thresholder utilizes the Minimum threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Moments](https://clij.github.io/clij2-docs/reference_thresholdMoments) -The automatic thresholder utilizes the Moments threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Otsu](https://clij.github.io/clij2-docs/reference_thresholdOtsu) -The automatic thresholder utilizes the Otsu threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Percentile](https://clij.github.io/clij2-docs/reference_thresholdPercentile) -The automatic thresholder utilizes the Percentile threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Renyi Entropy](https://clij.github.io/clij2-docs/reference_thresholdRenyiEntropy) -The automatic thresholder utilizes the RenyiEntropy threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Shanbhag](https://clij.github.io/clij2-docs/reference_thresholdShanbhag) -The automatic thresholder utilizes the Shanbhag threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Triangle](https://clij.github.io/clij2-docs/reference_thresholdTriangle) -The automatic thresholder utilizes the Triangle threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Threshold Yen](https://clij.github.io/clij2-docs/reference_thresholdYen) -The automatic thresholder utilizes the Yen threshold method implemented in ImageJ using a histogram determined on -the GPU to create binary images as similar as possible to ImageJ 'Apply Threshold' method. - -* [Top Hat Box](https://clij.github.io/clij2-docs/reference_topHatBox) -Applies a top-hat filter for background subtraction to the input image. - -* [Top Hat Sphere](https://clij.github.io/clij2-docs/reference_topHatSphere) -Applies a top-hat filter for background subtraction to the input image. - -* [Touching Neighbor Count Map](https://clij.github.io/clij2-docs/reference_touchingNeighborCountMap) -Takes a label map, determines which labels touch and replaces every label with the number of touching neighboring labels. - -* [Translate2D](https://clij.github.io/clij2-docs/reference_translate2D) -Translate an image stack in X and Y. - -* [Translate3D](https://clij.github.io/clij2-docs/reference_translate3D) -Translate an image stack in X, Y and Z. - -* [Transpose XY](https://clij.github.io/clij2-docs/reference_transposeXY) -Transpose X and Y axes of an image. - -* [Transpose XZ](https://clij.github.io/clij2-docs/reference_transposeXZ) -Transpose X and Z axes of an image. - -* [Transpose YZ](https://clij.github.io/clij2-docs/reference_transposeYZ) -Transpose Y and Z axes of an image. - -* [Undefined To Zero](https://clij.github.io/clij2-docs/reference_undefinedToZero) -Copies all pixels instead those which are not a number (NaN) or infinity (inf), which are replaced by 0. - -* [Variance Box](https://clij.github.io/clij2-docs/reference_varianceBox) -Computes the local variance of a pixels box neighborhood. - -* [Variance Sphere](https://clij.github.io/clij2-docs/reference_varianceSphere) -Computes the local variance of a pixels spherical neighborhood. - -* [Voronoi Labeling](https://clij.github.io/clij2-docs/reference_voronoiLabeling) -Takes a binary image, labels connected components and dilates the regions using a octagon shape until they touch. - -* [Voronoi Octagon](https://clij.github.io/clij2-docs/reference_voronoiOctagon) -Takes a binary image and dilates the regions using a octagon shape until they touch. - -* [Weka Label Classifier](https://clij.github.io/clij2-docs/reference_wekaLabelClassifier) -Applies a pre-trained CLIJx-Weka model to an image and a corresponding label map. - -* [Within Intensity Range](https://clij.github.io/clij2-docs/reference_withinIntensityRange) -Generates a binary image where pixels with intensity within the given range are 1 and others are 0. - -* [Z Position Of Maximum Z Projection](https://clij.github.io/clij2-docs/reference_zPositionOfMaximumZProjection) -Determines a Z-position of the maximum intensity along Z and writes it into the resulting image. - -* [Z Position Projection](https://clij.github.io/clij2-docs/reference_zPositionProjection) -Project a defined Z-slice of a 3D stack into a 2D image. - -* [Z Position Range Projection](https://clij.github.io/clij2-docs/reference_zPositionRangeProjection) -Project multiple Z-slices of a 3D stack into a new 3D stack. - -* [Zoom](https://clij.github.io/clij2-docs/reference_zoom) -See Scale2D and Scale3D. - - - -249 operations listed. - - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) diff --git a/save_and_load.md b/save_and_load.md deleted file mode 100644 index 0e996d7..0000000 --- a/save_and_load.md +++ /dev/null @@ -1,16 +0,0 @@ -## Saving and loaging workflows -Workflows can be saved as groovy scripts. These scripts allow you to re-open an earlier created workflow and continue to -work on it. -Just use the main menut `Plugins > ImageJ on GPU (CLIJx-Assistant) > Save image data flow (experimental)"` to save the -current configuration and analogously, `Plugins>ImageJ on GPU (CLIJx-Assistant), "Load image data flow (experimental)"` -to reopen an formely saved configuration. - - -[Download video](images/save_and_load_oxford.mp4) - -Note: These groovy files are not meant to be edited. If you want to create scripts for image analysis scripts for automation, -which can be edited, check out the [Export workflows as ImageJ Script](https://clij.github.io/assistant/macro_export) section. - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) \ No newline at end of file diff --git a/segmentation_cells.md b/segmentation_cells.md deleted file mode 100644 index 56c6904..0000000 --- a/segmentation_cells.md +++ /dev/null @@ -1,187 +0,0 @@ -# Cell segmentation based on membrane markers -Cell segmentation based on membrane markers is challenging because usually contrast between cell body and cell membrane is small. -To improve on this, typically background subtraction methods are applied, for example: -* Top Hat -* Subtract Gaussian Background -* Difference of Gaussian - -For increasing thickness of membranes a maximum filter may be helpful - -An important step afterwards is typically inverting the image, for example with: -* Subtract Image from scalar -* Invert - -Furthermore, spot detection techniques are applied to determine cell centers, for example: -* Find and label maxima -* Detect and label maxima - -Alternatively, thresholding methods may also be able to differentiate cell body and cell memranes, for example: -* Threshold DoG, which is a combination of Difference of Gaussian and manual thresholding - -Afterwards, labels as extended to mimic cell extesions, e.g. -* Extend labels with maximum Radius -* Extend labels via Voronoi - -It is recommended to utilize modern [GDDR6-based GPU hardware](https://clij.github.io/assistant/installation#hardware) for 3D segmentation. - -## How to do cell segmentation -Open your data set. [Start the CLIJx-Assistant](https://clij.github.io/assistant/getting_started) and follow such a workflows: - -* Your dataset - * CLIJx-Assistant Starting point - * [Optional: Noise removal] - * Subtract Image From Scalar - * Top Hat - * Threshold DoG - * Parametric Watershed - * Connected Components Labeling - * Extend Labeling Via Voronoi - * Pull To ROIManager - -After assembling your workflow, tune the parameters. Usually, only small values for sigma and radii are needed when segmenting cells based on -![Image](images/membrane_based_cell_segmentation.png) -[Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] - -## Example workflow -``` -This protocol documents an image processing workflow using CLIJx-Assistant. -Read more about it online: https://clij.github.io/incubator/ - -Overview - * Copy - * Subtract Image From Scalar - * Top Hat - * Threshold DoG - * Parametric Watershed - * Connected Components Labeling - * Extend Labeling Via Voronoi - * Pull To ROIManager - -We start by processing the image "strausberg_timelapse.tif" for simplicity, we call it image1. - - -As the next step we applied "Assistant Starting Point" on image1 and got a new image out, -image2, also titled "CLIJx Image of strausberg_timelapse.tif". -. - -Following, we applied "Generic Assistant Plugin" on image2 and got a new image out, -image3, also titled "Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif". -In detail, we used the parameter scalar = 255.0. - -As the next step we applied "Generic Assistant Plugin" on image3 and got a new image out, -image4, also titled "Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif". -In order to do so, we used the parameters radiusX = 10.0, radiusY = 10.0 and radiusZ = 10.0. - -As the next step we applied "Generic Assistant Plugin" on image4 and got a new image out, -image5, also titled "Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif". -Therefore, we used the parameters sigma1 = 1.0, sigma2 = 3.0, threshold = 10.0 and above_threshold = 1.0. - -Afterwards, we applied "Generic Assistant Plugin" on image5 and got a new image out, -image6, also titled "Parametric Watershed of Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif". -Therefore, we used the parameters sigma_x = 2.0, sigma_y = 2.0 and sigma_z = 2.0. - -Following, we applied "Generic Assistant Plugin" on image6 and got a new image out, -image7, also titled "Connected Components Labeling of Parametric Watershed of Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif". -. - -Then, we applied "Generic Assistant Plugin" on image7 and got a new image out, -image8, also titled "Extend Labeling Via Voronoi of Connected Components Labeling of Parametric Watershed of Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif". -. - -As the next step we applied "Pull To ROIManager" on image8 and got a new image out, -image9, also titled "ROIs of Extend Labeling Via Voronoi of Connected Components Labeling of Parametric Watershed of Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif". -. - - -``` - -## Example Macro - -``` -// To make this script run in Fiji, please activate -// the clij and clij2 update sites in your Fiji -// installation. Read more: https://clij.github.io - -// Init GPU -run("CLIJ2 Macro Extensions", "cl_device="); - -// Overview -// * Copy -// * Subtract Image From Scalar -// * Top Hat -// * Threshold DoG -// * Parametric Watershed -// * Connected Components Labeling -// * Extend Labeling Via Voronoi -// * Pull To ROIManager -// -image1 = "strausberg_timelapse.tif"; -Ext.CLIJ2_push(image1); - -// Assistant Starting Point -// image1 = "strausberg_timelapse.tif"; -// image2 = "CLIJx Image of strausberg_timelapse.tif"; -Ext.CLIJ2_copy(image1, image2); -Ext.CLIJ2_pull(image2); // consider removing this line if you don't need to see that image - -// Generic Assistant Plugin -// image2 = "CLIJx Image of strausberg_timelapse.tif"; -// image3 = "Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -scalar = 255.0; -Ext.CLIJ2_subtractImageFromScalar(image2, image3, scalar); -Ext.CLIJ2_pull(image3); // consider removing this line if you don't need to see that image - -// Generic Assistant Plugin -// image3 = "Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -// image4 = "Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -radiusX = 10.0; -radiusY = 10.0; -radiusZ = 10.0; -Ext.CLIJ2_topHatBox(image3, image4, radiusX, radiusY, radiusZ); -Ext.CLIJ2_pull(image4); // consider removing this line if you don't need to see that image - -// Generic Assistant Plugin -// image4 = "Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -// image5 = "Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -sigma1 = 1.0; -sigma2 = 3.0; -threshold = 10.0; -above_threshold = 1.0; -Ext.CLIJx_thresholdDoG(image4, image5, sigma1, sigma2, threshold, above_threshold); -Ext.CLIJ2_pull(image5); // consider removing this line if you don't need to see that image - -// Generic Assistant Plugin -// image5 = "Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -// image6 = "Parametric Watershed of Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -sigma_x = 2.0; -sigma_y = 2.0; -sigma_z = 2.0; -Ext.CLIJx_parametricWatershed(image5, image6, sigma_x, sigma_y, sigma_z); -Ext.CLIJ2_pull(image6); // consider removing this line if you don't need to see that image - -// Generic Assistant Plugin -// image6 = "Parametric Watershed of Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -// image7 = "Connected Components Labeling of Parametric Watershed of Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -Ext.CLIJ2_connectedComponentsLabelingBox(image6, image7); -Ext.CLIJ2_pull(image7); // consider removing this line if you don't need to see that image - -// Generic Assistant Plugin -// image7 = "Connected Components Labeling of Parametric Watershed of Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -// image8 = "Extend Labeling Via Voronoi of Connected Components Labeling of Parametric Watershed of Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -Ext.CLIJ2_extendLabelingViaVoronoi(image7, image8); -Ext.CLIJ2_pull(image8); // consider removing this line if you don't need to see that image - -// Pull To ROIManager -// image8 = "Extend Labeling Via Voronoi of Connected Components Labeling of Parametric Watershed of Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -// image9 = "ROIs of Extend Labeling Via Voronoi of Connected Components Labeling of Parametric Watershed of Threshold DoG of Top Hat of Subtract Image From Scalar of CLIJx Image of strausberg_timelapse.tif"; -Ext.CLIJ2_pullToROIManager(image8, image9); -Ext.CLIJ2_pull(image9); // consider removing this line if you don't need to see that image - - - -``` - - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/segmentation_nuclei.md b/segmentation_nuclei.md deleted file mode 100644 index cd7de71..0000000 --- a/segmentation_nuclei.md +++ /dev/null @@ -1,31 +0,0 @@ -# Nuclei segmentation -Nuclei segmentation in 3D data is challenging because of background intensity, uneven intensity in Z-dimension, noise -and simply the amoung of pixels which need to be processed. -Real-time experience while configuring a workflow for nuclei segmentation can be achieved when utilizing classical methods -such as filtering, thresholding and watershed techniques. -It is recommended to utilize modern [GDDR6-based GPU hardware](https://clij.github.io/assistant/installation#hardware) for 3D segmentation. - -## How to do 3D cell nuclei segmentation -Open your data set. [Start the CLIJx-Assistant](https://clij.github.io/assistant/getting_started) and follow such a workflows: - -* Your dataset - * CLIJx-Assistant Starting point - * [Optional: Noise removal and Background subtraction] - * Threshold DoG - * Parametric Watershed - * Connected Components Labeling - * Maximum Z projection - -After assembling your workflow, put these operations next to each other, change the parameters. - - -[Download video](images/incubator_segmentation_3d_nuclei.mp4) -[Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] - -There are many ways for detecting nuclei and extending their size, e.g. to study neighborhood relationships. - -[Download video](images/clijxa_teaser1.mp4) [Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/sphere_projection.md b/sphere_projection.md deleted file mode 100644 index 5a4df8c..0000000 --- a/sphere_projection.md +++ /dev/null @@ -1,55 +0,0 @@ -# Sphere projection -Similar to the [Cylinder projection](https://clij.github.io/assistant/cylinder_projection) -we can use a sphere projection to project intensities from a spherical sample into a 2D image. - -![Image](images/sphere_projection.png) - -## How to use a sphere projection on your data -When applying sphere projections to image stacks, a rigid dransform in advance is helpful to have control of the position and tilt of your sample in projected space. - -Open your time lapse data set. [Start the CLIJx-Assistant](https://clij.github.io/assistant/getting_started) and follow these steps: - -* Your dataset - * CLIJx-Assistant Starting point - * [Optional: Noise removal and Background subtraction] - * Make Isotropic - * Rigid transform - * Sphere transform - * Maximum Z projection - -After assembling your workflow, put the last three operations next to each other, change the parameters of the -rigid transform and inspect the results in the maximum Z projection. - - -[Download video](images/Assistant_rigid_sphere_projection.mp4) -[Image data source: Irene Seijo Barandiaran, Grapin-Botton lab, MPI CBG] - -# Half-sphere projection -In case of datasets which are more similar to a half-sphere, e.g. a Tribolium castaneum embryo imaged from anterior to posterior, -it may make sense to draw a half-sphere projection. - -![Image](images/Tribolium_Dunkirk_stack.gif) -![Image](images/Tribolium_Dunkirk_stack_MAX_Project.gif) -Stack view and corresponding maximum Z projection [Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] - -This can be achieved by moving the center of the transform to the front (0) or back (1) of the image stack. - -![Image](images/sphere_transform_dialog.png) - -This will result in a transformed stack like this: - -![Image](images/Sphere_projection_Tribolium_Dunkirk.gif) -[Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] - -And the maximum projection looks like this: -![Image](images/tribolium_pole_view.png) -[Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] - -If the maximum-Z-projection looks like this, the center is on the wrong end of the stack: -![Image](images/tribolium_pole_view_wrong.png) -[Image data source: Daniela Vorkel, Myers lab, CSBD / MPI CBG] - - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/supplementary_methods_section_generator.md b/supplementary_methods_section_generator.md deleted file mode 100644 index 410f490..0000000 --- a/supplementary_methods_section_generator.md +++ /dev/null @@ -1,18 +0,0 @@ -## Export human readable protocols -Just like [generating scripts](https://clij.github.io/assistant/macro_export), one can also -generate human readable protocols of workflows. Just click the right-click menu `Generate Script > Human readable protocol`. - -Note: As modern reference / citation checking tools might detect these generated texts as illegal citations, it is -recommended to mark these texts as generated. - -![Image](images/human_readable_protocol.png) - -## ImageJ Macro Markdown Notebooks - -You can also export your workflow as notebook using the right-click menu `Generate Script > ImageJ Macro Markdown`: - -[Download video](images/ijmmd.mp4) - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint) diff --git a/undo.md b/undo.md deleted file mode 100644 index 5243f21..0000000 --- a/undo.md +++ /dev/null @@ -1,12 +0,0 @@ -# Undo parameter changes -After every parameter change, all parameters of the current operation + all predecessors are saved to the parameter history. -You can go back to former parameter settings by selecting the setting from the `Info > Parameter history` menu. - -![Image](images/undo.png) - - -[Download video](images/undo.mp4) - -Back to [CLIJx-Assistant](https://clij.github.io/assistant) - -[Imprint](https://clij.github.io/imprint)