# Visualization

TODO biginfo-box: {"content"=>"See [:Category:Visualization](Category_Visualization) for pages about scientific visualization."} TODO learn: {"content"=>"techniques"} Scientific visualization is a set of techniques for graphically illustrating scientific data, enabling scientists to better understand, illustrate, and glean insight from their data.

## Getting Started with Simple Visualization Options in ImageJ

### Pseudocolor Image Look-Up Tables (LUTs)

A pseudocolor image is a single channel gray image (8, 16 or 32-bit) that has color assigned to it via a lookup table, i.e. a LUT. A LUT is a predefined table of gray values with matching red, green, and blue values so that shadows of gray are displayed as colorized pixels. Thus, differences in color in the pseudo-colored image reflect differences in intensity of the object rather than differences in color of the specimen that has been imaged.

The LUT Menu of ImageJ contains a large collection of lookup tables that can be applied to a pseudocolor image.

Note: in the ImageJ 1.x user interface, LUTs are always 8-bit. When working with an image of higher bit depth, its intensity values are binned into 256 levels between minimum and maximum (see section Brightness/Contrast), and the LUT is applied onto these binned levels.

More information on this topic can be found on the Color Image Processing page.

Fluorescence images are usually acquired without color information (i.e. by monochrome cameras or with photomultipliers): each channel contains just intensity values. To display a multi-channel fluorescence image in composite mode (i.e. an overlay of all channels), each channel can be assigned a monochrome false-color LUT, e.g. ‘Red’, ‘Green’, ‘Blue’, ‘Cyan’, ‘Magenta’, ‘Yellow’, etc.

When analyzing quantitative data in an image, a false-color LUT (in this case also referred to as color map) can increase the visibility of low-contrasted features in the image and help the human eye to compare different images.

Here’s a list of recommended options to choose a LUT:

 LUT Name Properties Common Usage Histogram mpl-viridis Perceptually uniform1Dark-to-Bright mapping Quantitative display of positive values on a continuous scale Histogram of the M51 Galaxy sample image with the mpl-viridis LUT applied HiLo Minimum of display range is blueMaximum of display range is redNormal gray-scale LUT for all other values Assessment of over-/under-exposure in an image Histogram of the M51 Galaxy sample image with the displayed range adjusted and the HiLo LUT applied phase Diverging color mapBright center, dark min and max Histogram of a randomized calibrated 8-bit image with the phase LUT applied

1. https://bids.github.io/colormap/↩︎

### Visualization of Volumetric Image Data

Here we summarize some of the 3D visualization plugins in ImageJ.

 Plugin Name Short Description Highlights Plugin Snapshot 3D Viewer A tool for hardware-accelerated visualization possibilities for image stacks, using the Java 3D library. Stacks can be displayed as texture-based volume renderings, surfaces, or orthoslicesMacro-recordable functionsAdjust the transfer functions, edit volumes, point lists, landmark-based registration, transformations, 3D Content in PDFs 3D_Viewer_overview.png ClearVolume A tool for live rendering volumetric multi-channel data. Create instant multiview and multicolor renderingsInstant rewind and replay of time-lapse recordingsProvides real-time GPU-based image processing capabilities, such as image sharpness estimation and sample drift trackingEnables live streaming of 3D data in real time over the internet Volume Viewer A tool for 3D reslicing and threshold-enabled 3D visualization. Non-hardware accelerated volume rendering in different modalities.Documentation SciView A tool for 3D visualization capabilities for images and meshes. Uses the Scenery and ClearVolume infrastructureIntegrates ImageJ2 functionality, including ImageJ OpsAims to serve as a modern replacement to 3D Viewer

## Making Plots in ImageJ

### The Basics: ImageJ v1.x Plot Tools

1. Plot Profile
2. [https://imagej.nih.gov/ij/docs/guide/146-28.html#sub:Plot-Z-axis-Profile… Plot Z-axis Profile]
3. [https://imagej.nih.gov/ij/docs/guide/146-30.html#sub:Surface-Plot… Surface Plot]

### Plotting tools available via scripting…

1. JFreeChart (used by Directionality)
2. Matplotlib

## Making Figures in ImageJ

### Available Plugins for Making Figures in ImageJ

Here we summarize some of the ‘big data’ visualization plugins in ImageJ.

 Plugin Name Short Description Highlights Plugin Snapshot BigDataViewer A tool for interactive browsing of arbitrarily large image datasets. Arbitrary slicing, zooming, etc.For multi-angle, multi-channel, time-series, etc.Adding overlays, annotations, etc.Reusable software componentsUsed as data backend and/or visualization frontend by MultiView Reconstruction, MaMuT, BigWarp, etc. MultiView Reconstruction A tool for registration, fusion, deconvolution, and visualization of multiview microscopy images. Designed for lightsheet fluorescence microscopyApplicable to any form of three or higher dimensional imaging modalitiesInteractive viewing and annotation of the data

## Interactive Analysis Plugins based on ‘Big Data’ Visualization Tools

Here we summarize the more advanced analysis plugins in ImageJ using the above ‘big data’ visualization tools.

 Plugin Name Short Description More Details... Plugin Snapshot MaMuT (Massive Multi-view Tracker) A tool for manual and semi-automatic tracking in multiple views. Allows browsing, annotating, and curating annotations for large image dataCombines BigDataViewer and TrackMateShips TrackScheme, the lineage browser taken from TrackMate BigWarp A tool for manual, interactive, landmark-based deformable image alignment. Uses BigDataViewer for visualization and navigationUses a Thin Plate Spline implemented in Java to build a deformation from point correspondencesEnables landmark pair placement and displays the effects of the warp on-the-fly

[1] http://peterkovesi.com/projects/colourmaps/

[2] http://www.kennethmoreland.com/color-maps/