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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.

In the ImageJ 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
  • Perceptually uniform1
  • Dark-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
  • Minimum of display range is blue
  • Maximum of display range is red
  • Normal 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
  • Diverging color map
  • Bright center, dark min and max
Histogram of a randomized calibrated 8-bit image with the phase LUT applied


For further reading about color maps, see [1] and [2]

Adjusting your Image in ImageJ

The Adjust submenu contains commands that adjust Brightness/Contrast, Threshold levels, and image Size.

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 orthoslices
  • Macro-recordable functions
  • Adjust the transfer functions, edit volumes, point lists, landmark-based registration, transformations, 3D Content in PDFs
ClearVolume A tool for live rendering volumetric multi-channel data.
  • Create instant multiview and multicolor renderings
  • Instant rewind and replay of time-lapse recordings
  • Provides real-time GPU-based image processing capabilities, such as image sharpness estimation and sample drift tracking
  • Enables 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.

Making Plots in ImageJ

The Basics: ImageJ v1.x Plot Tools

  1. Plot Profile
  2. Plot Z-axis Profile
  3. Surface Plot

Available Plugins for Making Plots in ImageJ

  1. Quiver Plot

Plotting tools available via scripting…

  1. JFreeChart (used by Directionality)
  2. Matplotlib

Making Figures in ImageJ

Available Plugins for Making Figures in ImageJ

  1. Magic Montage
  2. FigureJ
  3. ScientiFig
  4. Figure Maker

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 components
  • Used 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 microscopy
  • Applicable to any form of three or higher dimensional imaging modalities
  • Interactive 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.
BigWarp A tool for manual, interactive, landmark-based deformable image alignment.
  • Uses BigDataViewer for visualization and navigation
  • Uses a Thin Plate Spline implemented in Java to build a deformation from point correspondences
  • Enables landmark pair placement and displays the effects of the warp on-the-fly