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Port some documents from the old website.
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‎_config.yml‎

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# Build settings
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markdown: kramdown
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theme: minima
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#theme: just-the-docs
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#theme: minima
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theme: just-the-docs
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#remote_theme: pmarsceill/just-the-docs
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color_scheme: "dark"
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plugins:
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- jekyll-feed
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# just-the-docs options
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search_enabled: true
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# Aux links for the upper right navigation
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aux_links:
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"IJ-Plugins on GitHub":
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- "//github.com/ij-plugins"
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# Exclude from processing.
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# The following items will not be processed, by default. Create a custom list

‎assets/images/ijp-toolkit/3D-Toolkit/brainweb1_31.jpg‎

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‎assets/images/ijp-toolkit/3D-Toolkit/grow_options_dialog.jpg‎

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‎assets/images/ijp-toolkit/3D-Toolkit/segmented_brainweb.jpg‎

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‎docs/ijp-toolkit/3D-IO/3D-IO.md‎

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---
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layout: default
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title: 3D IO
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nav_order: 1
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parent: IJ-Plugins Toolkit
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has_children: true
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permalink: /docs/ijp-toolkit/3D-IO
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---
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## 3D IO
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Plugins for reading and writing in 3D formats:
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* **MetaImage Reader** - reads from MetaImage format used by <a href="http://itk.org/">ITK
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* **MetaImage Writer** - writes a 2D image or a 3D stack to MetaImage format used by <a href="http://itk.org/">ITK</a>.
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Option <strong>Save in single file</strong> indicates whether the image should be saved in a single file
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(extension *.mha) or the header and the image data should be saved in separate files (with extensions *.mhd
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and *.raw respectively.)
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* **VTK Reader** - reads format used by <a href="http://vtk.org/">VTK</a>.
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* **VTK Writer** - writes a 2D image or a 3D stack in format used by <a href="http://vtk.org/">VTK</a>.
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* **Export as STL** - interprets intensity in 2D image as height and writes the height surface in
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<a href="http://en.wikipedia.org/wiki/STL_%28file_format%29">STL format</a>. Data can be saved either in
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<samp>binary</samp> or <samp>ascii</samp> (text) format. Binary format produces much smaller files.
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Option <strong>Save sides</strong> enables generation of the mesh for sides and the bottom.
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Plugins install under `Plugins > 3D IO` menu.

‎docs/ijp-toolkit/3D-IO/Export-as-STL.md‎

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---
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layout: default
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title: Export as STL
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nav_order: 2
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grand_parent: IJ Plugins Toolkit
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parent: 3D IO
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permalink: /docs/ijp-toolkit/3D-IO/Export-as-STL
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---
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## Export as STL
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<strong>Export as STL</strong> - interprets intensity in 2D image as height and writes the height surface in
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<a href="http://en.wikipedia.org/wiki/STL_%28file_format%29">STL format</a>. Data can be saved either in
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<samp>binary</samp> or <samp>ascii</samp> (text) format. Binary format produces much smaller files.
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Option <strong>Save sides</strong> enables generation of the mesh for sides and the bottom.
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The plugins install under <tt>Plugins</tt> &gt; <tt>3D IO</tt> &gt; <tt>Export as STL</tt>menu.
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‎docs/ijp-toolkit/3D-Toolkit/3D-Toolkit.md‎

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---
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layout: default
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title: 3D Toolkit
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nav_order: 2
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parent: IJ-Plugins Toolkit
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has_children: true
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permalink: /docs/ijp-toolkit/3D-Toolkit
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---
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## 3D Toolkit
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Those filters interpret an image stack as a 3D image.
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<ul>
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<li><strong>Connected Threshold Grower</strong> - region growing segmentation (<a
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href="example-connected-threshold-growing.html">example</a>).
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</li>
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<li><strong>Auto Clip Volume</strong> - Auto volume clipping to a region that contains non-zero pixels.</li>
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<li><strong>Morphological Dilate 3D</strong> - grey level dilation (max).</li>
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<li><strong>Morphological Erode 3D</strong> - grey level erosion (min).</li>
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<li><strong>Median 3D</strong> - correctly processes all pixes including borders.</li>
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</ul>
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Plugins install under <tt>Plugins</tt> &gt; <tt>3D Toolkit</tt> menu.

‎docs/ijp-toolkit/3D-Toolkit/example-connected-threshold-growing.md‎

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---
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layout: default
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title: Connected Threshold Growing Example
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nav_order: 2
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grand_parent: IJ Plugins Toolkit
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parent: 3D Toolkit
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permalink: /docs/ijp-toolkit/3D-Toolkit/example-connected-threshold-growing
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---
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## Connected Threshold Growing Example
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The Connected Threshold Growing plugin can be used to perform
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segmentation of 2D and 3D images. The plugin accepts 8 bit and 16 bit gray
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images. To perform segmentation you specify location of seed point (x,y,z),
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and minimum and maximum limits on pixel intensity. Segmented region will
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contain all pixels connected to the seed point which intensity is within
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minimum/maximum intensity limits.</p>
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![Grow options dialog]({{ site.url }}/assets/images/ijp-toolkit/3D-Toolkit/grow_options_dialog.jpg)
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The example demonstrates segmentation of a 3D MRI image from
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<a href="http://www.bic.mni.mcgill.ca/brainweb/">BrainWeb Project</a>.
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Here is slices 31 of an image used in the example:
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![Input image]({{ site.url }}/assets/images/ijp-toolkit/3D-Toolkit/brainweb1_31.jpg)
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The image was segmented using only 7 seed points (white matter, gray
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matter, skull, left eye, left lens, right eye, and right lens). White and
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gray matter regions were combined before 3D rendering using ImageJ image
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calculator.
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(<a href="SmoothRender.java.html">SmoothRender.java</a>)
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![Segmented image]({{ site.url }}/assets/images/ijp-toolkit/3D-Toolkit/segmented_brainweb.jpg)
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The above 3D rendering was performed using following Java code and VTK library:
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```java
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import vtk.*;
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import javax.swing.*;
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import java.awt.*;
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import java.awt.event.WindowAdapter;
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import java.awt.event.WindowEvent;
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public class SmoothRender extends JPanel {
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static {
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// Load VTK MarchingCubes library, rest is loaded by vtkPanel
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System.loadLibrary("vtkPatentedJava");
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}
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private final String PREFIX = "/data/";
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/**
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* Constructor.
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*/
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public SmoothRender() {
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// Setup VTK rendering panel
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vtkPanel renWin = new vtkPanel();
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vtkRenderer ren = renWin.GetRenderer();
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// Create actors for each segmented region
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vtkActor skullActor = createActor(PREFIX + "brainweb1_skull_smooth.vtk", 127, 0.5, 500);
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skullActor.GetProperty().SetOpacity(0.25);
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skullActor.GetProperty().SetColor(1, 1, 1);
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skullActor.GetProperty().SetDiffuse(0.75);
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skullActor.GetProperty().SetSpecular(0.75);
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ren.AddActor(skullActor);
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vtkActor brainActor = createActor(PREFIX + "brainweb1_both_matters.vtk", 127, 0.5, 500);
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brainActor.GetProperty().SetColor(0.9961, 0.75, 0.1);
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brainActor.GetProperty().SetSpecular(1);
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ren.AddActor(brainActor);
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vtkActor rightEyeActor = createActor(PREFIX + "brainweb1_right_eye.vtk", 127, 0.5, 500);
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rightEyeActor.GetProperty().SetColor(1, 1, 1);
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rightEyeActor.GetProperty().SetDiffuse(0.95);
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rightEyeActor.GetProperty().SetSpecular(0.1);
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ren.AddActor(rightEyeActor);
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vtkActor rightRetinaActor = createActor(PREFIX + "brainweb1_right_eye_lens.vtk", 127, 0.10, 100);
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rightRetinaActor.GetProperty().SetColor(0.1, 0.1, 0.1);
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rightRetinaActor.GetProperty().SetSpecular(1);
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ren.AddActor(rightRetinaActor);
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vtkActor leftEyeActor = createActor(PREFIX + "brainweb1_left_eye_smooth.vtk", 127, 0.5, 500);
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leftEyeActor.GetProperty().SetColor(1, 1, 1);
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leftEyeActor.GetProperty().SetDiffuse(0.95);
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leftEyeActor.GetProperty().SetSpecular(0.1);
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ren.AddActor(leftEyeActor);
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vtkActor leftRetinaActor = createActor(PREFIX + "brainweb1_left_eye_lens.vtk", 127, 0.10, 100);
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leftRetinaActor.GetProperty().SetColor(0.1, 0.1, 0.1);
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leftRetinaActor.GetProperty().SetSpecular(1);
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leftRetinaActor.GetProperty().SetSpecularPower(1);
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ren.AddActor(leftRetinaActor);
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// Place renWin in the center of this panel
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setLayout(new BorderLayout());
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add(renWin, BorderLayout.CENTER);
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}
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/**
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* Load region data from a file and create its rendering pipeline.
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*/
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private vtkActor createActor(String fileName,
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int threshold,
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double targetReduction,
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int smoothingIterations) {
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vtkStructuredPointsReader reader = new vtkStructuredPointsReader();
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reader.SetFileName(fileName);
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reader.Update();
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vtkMarchingCubes surfaceExtractor = new vtkMarchingCubes();
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surfaceExtractor.SetInput(reader.GetOutput());
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surfaceExtractor.SetValue(0, threshold);
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surfaceExtractor.ComputeNormalsOn();
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vtkDecimatePro deci = new vtkDecimatePro();
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deci.SetInput(surfaceExtractor.GetOutput());
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deci.SetTargetReduction(targetReduction);
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deci.PreserveTopologyOn();
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vtkSmoothPolyDataFilter smoother = new vtkSmoothPolyDataFilter();
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smoother.SetInput(deci.GetOutput());
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smoother.SetNumberOfIterations(smoothingIterations);
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vtkPolyDataNormals normals = new vtkPolyDataNormals();
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normals.SetInput(smoother.GetOutput());
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normals.FlipNormalsOn();
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vtkPolyDataMapper mapper = new vtkPolyDataMapper();
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mapper.SetInput(normals.GetOutput());
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mapper.ScalarVisibilityOff();
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vtkActor actor = new vtkActor();
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actor.SetMapper(mapper);
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return actor;
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}
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/**
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*
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*/
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public static void main(String[] args) {
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SmoothRender panel = new SmoothRender();
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JFrame frame = new JFrame("SmoothRender");
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frame.addWindowListener(new WindowAdapter() {
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public void windowClosing(WindowEvent e) {
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System.exit(0);
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}
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});
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frame.getContentPane().add("Center", panel);
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frame.pack();
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frame.setVisible(true);
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}
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}
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```
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‎docs/ijp-toolkit/ijp-toolkit.md‎

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---
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layout: default
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title: IJ-Plugins Toolkit
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nav_order: 3
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has_children: true
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permalink: /docs/ijp-toolkit
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---
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IJ-Plugins Toolkit is a set of plugins for ImageJ. The plugins are grouped into:
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* 3D IO - import and export of data in 3D formats.
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* 3D Toolkit - operations on stacks interpreted as 3D images, including morphological operations.
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* Color - color space conversion, color edge detection (color and multi-band images).
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* Filters - fast median filters, coherence enhancing diffusion, and various anisotropic diffusion filters.
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* Graphics - Texture Synthesis - A plugin to perform texture synthesis using the image quilting algorithm of Efros and Freeman.
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* Segmentation - image segmentation through clustering, thresholding, and region growing.

‎index.md‎

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layout: home
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---
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## News
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Site under construction. May change or go down frequently.
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{: .label .label-yellow }
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## Updates
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* Mar 1, 2019 - the IJ-Plugins website started its transition from the [old location](https://ij-plugins.sourceforge.net/) to here
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## ImageJ Plugin Bundles and Libraries
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* **[ij-Plugins Toolkit](https://github.com/ij-plugins)** - Set of ImageJ plugins and library for: reading and writing of 3D images, morphological operations on 3D images, color space conversion, color edge detection, various anisotropic diffusion filters, fast median filters, image quilting, image segmentation through clustering and region growing, and more.
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* **[ijp-toolkit](https://github.com/ij-plugins/ijp-toolkit)** - Set of ImageJ plugins and library for: reading and writing of 3D images, morphological operations on 3D images, color space conversion, color edge detection, various anisotropic diffusion filters, fast median filters, image quilting, image segmentation through clustering and region growing, and more.
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* **[ijp-color](https://github.com/ij-plugins/ijp-color)** - Operations on color spaces and color images, color conversion, and color calibration.
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* **[ijp-DeBayer2SX](https://github.com/ij-plugins/ijp-DeBayer2SX)** - Bayer-pattern image to color image converters.
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* **[ijp-scala-console](https://github.com/ij-plugins/ijp-scala-console)** - edit and run Scala scripts from ImageJ, have full access to ImageJ API and API's of all installed plugins.

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