Visualization of 2D data

Author

Agustin Corbat

Published

January 5, 2026

2D Image Visualization Workshop: Fiji & Napari

Table of Contents

What is Fiji?

Fiji (Fiji Is Just ImageJ) is a free, open-source image processing and analysis platform built on top of ImageJ. It’s widely used in scientific research, particularly in microscopy, medical imaging, and general image analysis.

Key Features

  • User-friendly GUI - No programming required for basic operations
  • Pre-installed plugins - Comes with a curated selection of image processing tools
  • Extensible - Can add additional plugins for specialized tasks
  • Multi-format support - Handles TIFF, JPEG, PNG, BMP, and many scientific formats
  • Batch processing - Automate workflows with macros
  • Active community - Extensive documentation and forum support

Common Use Cases

  • Microscopy image analysis
  • Segmentation and thresholding
  • Intensity measurements and statistics
  • Image registration and stitching
  • 3D image visualization
  • Time-series analysis

Getting Started with Fiji

Download and Installation

Windows, Mac, or Linux

  1. Visit the official Fiji website: https://fiji.sc/
  2. Click “Download” and select your operating system
  3. Extract the downloaded file to your preferred location (preferably somewhere admin rights are not required to modify files)
  4. Double-click Fiji.exe (Windows) or the Fiji application bundle to launch

No additional installation required - Fiji is portable and comes with Java bundled.

Verify Installation

When you launch Fiji, you should see: - The main ImageJ window (toolbar with various tools) - A menu bar with File, Edit, Image, Process, Analyze, Plugins, Window, Help

Fiji window

Fiji window

Setting Up Sample Data

For this workshop, we recommend having sample images available. You can: - Use your own microscopy images or photographs - Use Fiji sample data - Download sample images from: - Fiji sample images - Scientific image repositories (e.g., Open Microscopy Environment)

Opening and Viewing Images

Opening an Image

The easiest way is to use drag-and-drop on the gray area.

Another option is to:

  1. File → Open (or Ctrl+O / Cmd+O)
  2. Navigate to your image file and select it
  3. Click Open

The image will display in a new window with the filename in the title bar.

In many cases, the BioFormats Plugin will be automatically called to open the image.

BioFormats Plugin

BioFormats Plugin

How many of these options do we understand?

Basic Viewing Tools

Tool Function Shortcut
Zoom In/Out Adjust magnification to inspect details or see whole image + / - or scroll wheel
Pan Move around the image when zoomed in Click and drag with spacebar
Color Picker Sample pixel values I key
Measure Determine distances and areas M key
ROI (Region of Interest) Select rectangular, oval, or free-form areas Toolbar buttons

Excercise

Zoom in as much as you can into a small region. What do you see? Are the squares part of the sample or a rendering? If you hover your mouse over some pixels, what do you see at the bottom grey area of the Fiji main window?

Checking Image Properties

After opening an image, first check its header. What are the values on top?

Can you check its metadata? (go to Image>Show Info...)

Show Info Window in Fiji

Show Info Window in Fiji

Fill in the following table:

Metadata Item Value
Pixel Size (µm)
Magnification
Numerical Aperture (NA)
Bit Depth
Image Dimensions (width × height)
Number of Channels
Number of Slices (if z-stack)
Voxel Dimensions
Unit

Changing the Bit Depth

Images can be stored in different formats representing different levels of intensity information:

  • 8-bit (256 intensity levels) - Smallest file size, fastest processing, good for display
  • 16-bit (65,536 intensity levels) - More detail, larger file size, common in microscopy
  • 32-bit (floating-point) - Highest precision, largest file size, used for calculations

To change the bit depth of your image:

  1. Go to Image > Type
  2. Select your desired bit depth: 8-bit, 16-bit, or 32-bit
  3. If converting from higher to lower bit depth (e.g., 16-bit to 8-bit), you may lose information

Exercise

Why might you want to convert a 16-bit image to 8-bit? Consider factors like file size, processing speed, memory usage, and the type of analysis you plan to do. When would it be a bad idea?

Essential Fiji Functions

Duplicate an image

Most of the operations are applied on the currently selected image, which might change its intensity values. Some operations cannot be undone, which means that you have to reopen the image and go through the whole process again if something goes wrong. To avoid this, it’s a good idea to duplicate the image just in case.

Go to Image > Duplicate.

Adjusting Brightness & Contrast

  1. Image > Adjust > Brightness/Contrast...
  2. Drag sliders to enhance visibility
  3. Check Auto for automatic adjustment
  4. Use Reset to revert to original

Brightness and Contrast window

Brightness and Contrast window

Use case: Make dim images more visible for analysis.

Excercise

Try clicking on Apply to see what happens to the pixel values.

Changing the LookUp Table

Most of the cameras or detectors are monochromatic, which means that the color with which images are represented is a false color. Go to Image > LookUp Tables or click on the LUT button and choose a different color. What do you think the different palettes are for? Try for example HiLo, cool and 16-colors.

Working with more dimensions

First choose an image with several dimension, like a z-stack with more than one channel. If you don’t have one, you can choose File > Open samples > Mitosis (5D stack).

5D stack image

5D stack image

The sliders at the bottom allow you to go through the different channels, z-planes and timepoints. Some of the sample images already have what is called a composite, where channels are shown simultaneously. Try doing the following:

Split Channels

  1. Image > Color > Split Channels
  2. Each channel opens in a separate window
  3. Analyze or process channels independently

Merge Channels

  1. Image > Color > Merge Channels
  2. Select source windows for each channel
  3. Do not create a composite image

Channel Visualization

  1. Adjust color assignments for better visualization
  2. Go to Image > Color > Make Composite

Quick Measurements

Choose any structure in your image.

  1. Click on straight line button
  2. Draw a line across your object of interest
  3. Go to Analyze > Plot Profile
  4. Displays intensity values along the line (useful for colocalization studies)

Line profile image

Line profile image

Plot Profile image

Plot Profile image

What happens if you right click on the straight line button?

Measure Intensity

  1. Select an ROI (Region of Interest) using selection tools
  2. Analyze → Measure (or Ctrl+M)
  3. Results include: Area, Mean intensity, Std Dev, Min, Max

Crop

  1. Use rectangle selection tool to define region
  2. Image → Crop
  3. Image is reduced to selected area

Alternative: Napari in Jupyter Notebooks

If you prefer a Python-based, interactive approach, napari is a fast, interactive image viewer for multi-dimensional images.

Installation

Pixi is a package manager that makes it easy to manage Python environments and dependencies. Follow these steps to set up Napari with pixi:

Install Pixi

  1. Visit the official Pixi website: https://pixi.sh/

  2. Download and install Pixi for your operating system (Windows, Mac, or Linux)

  3. Verify installation by running in your terminal:

    pixi --version

Initialize a Pixi Project

  1. Create a new directory for your image analysis project:

    mkdir my-image-analysis
cd my-image-analysis

  2. Initialize a Pixi project:

    pixi init

  3. This creates a pixi.toml configuration file for your project.

Add Python and Napari to Your Environment

Add Python, napari, and Jupyter to your project:

pixi add python napari jupyter scikit-image

This command: - Installs Python and the specified packages - Creates a pixi.lock file with exact versions for reproducibility - Sets up an isolated environment specific to your project

Running Jupyter with Pixi

Start Jupyter notebook directly from your pixi project:

pixi run jupyter notebook

This runs Jupyter within your project’s environment, giving it access to napari, scikit-image, and all other packages you added.

Basic Napari Workflow

Import and Display an Image

import napari
from skimage import io

# Load an image
image = io.imread('path/to/image.tif')

# Create viewer and add image layer
viewer = napari.view_image(image, name='raw image')

Interactive ROI Selection

We can do this either programatically from within the Jupyter notebook, or we can do this interactively from the Napari GUI.

import napari
from skimage import io

image = io.imread('path/to/image.tif')

viewer = napari.Viewer()
layer = viewer.add_image(image)

# Add shapes layer for ROI drawing
shapes_layer = viewer.add_shapes(
    shape_type='rectangle',
    edge_color='cyan',
    face_color='transparent',
    name='ROIs'
)

Or:

  1. Click on the button to generate a new Shapes Layer
  2. Add a new rectangle

Once the rectangle is made, try accessing it via a cell in the Jupyter notebook. Access drawn shapes via shapes_layer.data.

Napari Advantages

  • Interactive - Real-time parameter adjustment
  • Jupyter integration - Works in notebooks for reproducible workflows
  • Python-native - Easy integration with scientific Python stack (scikit-image, scipy, etc.)
  • Multi-dimensional - Handles time series, z-stacks, and multi-channel data
  • Programmatic ROI - Extract measurements from selected regions in code

Napari Limitations vs. Fiji

  • Less pre-built analysis functions (requires coding)
  • Smaller ecosystem of plugins compared to Fiji
  • Learning curve for Python programming

Additional Workshop Exercises

Exercise 1: Basic Image Inspection (Fiji)

  1. Download a sample image
  2. Open in Fiji
  3. Check image properties (dimensions, bit depth, channels)
  4. Use zoom and pan tools to explore
  5. Measure a feature using the measure tool

Exercise 2: Image Enhancement (Fiji)

  1. Open a dim or low-contrast image
  2. Apply Brightness/Contrast adjustment
  3. Apply Gaussian blur to reduce noise
  4. Use Unsharp Mask to enhance edges
  5. Compare original and processed versions

Exercise 3: Segmentation (Fiji or Napari)

  1. Open a 2D image with distinct objects
  2. Apply threshold to create binary image
  3. Use morphological operations (Erode/Dilate) to clean up
  4. Run Analyze Particles to count and measure objects
  5. Generate summary statistics

Exercise 4: Reproducible Analysis (Napari in Jupyter)

  1. Load an image using scikit-image
  2. Apply preprocessing (blur, threshold)
  3. Perform segmentation
  4. Visualize results with multiple layers in napari
  5. Extract measurements from labeled regions

Tips & Tricks

Fiji Performance

  • For very large images, reduce memory usage via Edit → Options → Memory & Threads
  • Disable auto-update of image window to speed up processing: Edit → Options
  • Use 8-bit images instead of 16-bit when precision isn’t critical

Reproducibility

  • Document your workflow by recording macro actions: Plugins → Macros → Record
  • Export processing steps as macro code for batch processing

Jupyter Best Practices

  • Save images and results to standardized output directories
  • Use consistent naming conventions for variables
  • Document parameters used for each processing step
  • Version your analysis notebooks in git

Resources

  • Official Fiji Documentation: https://fiji.sc/
  • ImageJ User Guide: https://imagej.net/
  • Napari Documentation: https://napari.org/
  • Scientific Image Analysis Tutorials: https://imagej.net/Tutorials
  • Scikit-image (Python): https://scikit-image.org/

Citation

Corbat, A. A. (2026). Workshop on 2D Image Visualization: Fiji and Napari. Zenodo. https://doi.org/10.5281/zenodo.18199646

License

This work is licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).

For more details, visit: https://creativecommons.org/licenses/by/4.0/ and https://github.com/acorbat/2D_visualization_workshop

Last Updated: January 2, 2026