Introduction to Image Analysis with Fiji

Peter Sobolewski (he/him)
Systems Analyst, Imaging Applications - Research IT

Use to advance the slide

Thanks to:

• Erick Ratamero (JAX), who prepared the original of these materials
• Dave Mason, (formerly) from the University of Liverpool, who prepared the original materials Erick adapted
• Unfortunately Dave's original slides are lost to time, but they looked very much like these.

• These slides: https://thejacksonlaboratory.github.io/fiji_workshops/IntroFiji.html
• The JAX Fiji workshops landing page: https://thejacksonlaboratory.github.io/fiji_workshops/
• Navigation: arrow keys left and right to navigate
• `m` key to get to navigation menu—browser back button won't work between slides
• Escape for slide overview

• Fiji & Bioimage analysis resources:
• Fiji Website
• Fiji Training Manual by Cameron Nowell, Monash University
• Introduction to Bioimage Analysis online book
• Where to find help after the workshop:
• image.sc Forum
• #image-analysis Slack channel
• Imaging Applications Coffee Hour

How an Image is formed

Understanding digital images

Widefield and Confocal microscopes acquire images in different ways.

Widefield and laser-scanning microscopes acquire images in different ways.

Detectors collect photons and convert them to a voltage

The A/D converter determines the dynamic range of the data

Unless you have good reason not to, always collect data at the highest possible bit depth

32 bit is a special data type called floating point.

TL;DR: pixels can have non-integer values which can be useful in applications like ratiometric imaging.

Introduction to ImageJ & Fiji

A cross platform, open source, Java-based image processing program

Learn more about Bio-Formats here

A word about updating

• Fiji includes a built-in updater mechanism, which may prompt you at launch:

• You can access it using [Help > Update...]
• This will also update installed plugins
• In the middle of a project, best to be conservative and not update—barring bugs
• Note: you can duplicate your Fiji and have a backup!

Hands on With Fiji

Getting to know the interface, info & status bars, calibrated vs non-calibrated images

Exercises will be provided in-line, as links to PDFs—right-click and open in a new tab

Commands on the Fiji menu will look like: [File > Save]

Sample/exercise data will look like this: 01-Photo.tif
Right-click to copy the URL and use [File > Import > URL...] to open

(Paste in the URL in the resulting box)

Exercise 1

1. Open Task1.pdf and follow the instructions there.
2. You will need these two images: 01-Photo.tif and 02-Biological_Image.tif

Calibration

Basic Manipulations

Intensity and Geometric adjustments

Images are an array of intensity values. The intensity histogram shows the number (on the y-axis) of each intensity value (on the x-axis) and thus the distribution of intensities

Photos typically have a broad range of intensity values and so the distribution of intensities varies greatly

Fluorescent micrographs will typically have a much more predictable distribution:

The Black and White points of the histogram dictate the bounds of the display (changing these values alters the brightness and contrast of the image)
They are often called "the contrast limits"

• Brightness: horizontal position of the display window
• Contrast: distance between the black and white point

The histogram is now stretched and the intensity value of every pixel is effectively doubled which increases the contrast in the image

If we repeat the same manipulation, the maximum intensity value in the image is now outside the bounds of the display scale!

Values falling beyond the new White point are dumped into the top bin of the histogram (IE 256 in an 8-bit image) and information from the image is lost
This is often called "clipping"

The best advice is to get it right during acquisition and make sure you compare apples to apples

Measurements and scale bars

Making measurements, what to measure, line vs area, adding a scale bar

The measurements provided are set via [Analyze > Set Measurements...] except for selection-specific measurements (length, angle, coords)

• [Analyze > Tools > ROI Manager...] to open the ROI Manager
• After making a selection (any type), press `Add` in the ROI Manager or hit 't' or run [Edit > Selection > Add to Manager]
• Repeat as needed for your ROI
• Run [More > Multi-measure]
• Use [More > Save] for better data provenance!

What if my image is uncalibrated?

Stacks

Understanding how Fiji deals with multidimensional images

Some file formats (eg. TIF) can store multiple images in one file which are called stacks

When more than one dimension (time, z, channel) is included, the images are still stored in a linear stack so it's critical to know the dimension order (eg, XYCZT, XYZTC etc) so you can navigate the stack correctly.

You will very rarely have to deal with Interleaved stacks because of Hyperstacks which give you independent control of dimensions with additional control bars.

Convert between stack types with the [Image > Hyperstack] menu

Multichannel images are handled as stacks

Interacting with channels is so common that there is a dedicated Channels Tool for additional controls:

• [Image > Color > Channels Tool]

Other useful menu options:

• [Image > Type > RGB Color]: convert from 3 channel stack to RGB
• [Image > Type > RGB Stack]: split RGB image into 3 channel stack

Exercise 4

• Open Task4.pdf and follow the instructions there.
• You will need this image: 06-MultiChannel.tif

Color in Digital Imaging

What is color? How and when to use LUTs

Color in your images is (almost always) dictated by arbitrary lookup tables

Lookup tables (LUTs) translate an intensity (1-256 for 8 bit) to an RGB display value

Color in your images is (almost always) dictated by arbitrary lookup tables

Lookup tables (LUTs), also called "colormaps" translate an intensity (1-256 for 8 bit) to an RGB display value

You can use whatever colours you want (they are arbitrary after all), but the most reliable contrast is greyscale

More info on color and sensitivity of the human eye here

Additive and Subtractive Colours can be mixed in defined ways

Non 'pure' colours cannot be combined in reliable ways (as they contain a mix of other channels)

BUT! Interpretation is highly context dependent!

https://en.wikipedia.org/wiki/The_dress

Open 05-BlueGreenSwirl.png

~10% of the population have trouble discerning Red and Green. Consider using Green and Magenta instead which still combine to white.

A couple of useful LUTs:

Applications

• Segmentation
• Colocalisation
• Tracking
• Stitching
• Batch Processing

Applications: Segmentation

What is segmentation? thresholding, Connected Component Analysis

Segmentation is the separation of an image into regions of interest
Semantic segmentation assigns each pixel to a class, e.g. foreground vs. background

The end point for most segmentation is a binary mask (false/true, 0/255)

Fiji has an odd way of dealing with masks

Run [Process > Binary > Options] and check Black Background. Hit OK.

For most applications, intensity-based thresholding works well. This relies on the signal being higher intensity than the background.

We use a Threshold to pick a cutoff.

One approach to identifying individual objects when given a binary mask is Connected Component Analysis/Labeling (CCA)

This approach assigns pixels that are touching (connected) to individual object:

In Fiji this can be accomplished using [Analyze > Analyze Particles...]

If CCA results in merged objects due to touching, then "watershed" approach is frequently used to define boundaries.

Exercise 6

Open Task6.pdf and follow the instructions there.

You will need these images: 07-nuclei.tif and 08-nucleiMask.tif

Analyze Particles comes with the option to Display Results

• The results table will have a column for anything selected in [Analyze > Set Measurements] and one row per object
• Measurements (including intensity - labelled purple above) are made on the mask!

To apply measurements to the original image: check Add to Manager in Analyze Particles, open the original image, then run [More > Multi Measure]

Don't forget [Analyze > Set Measurements] to pick parameters

You may want to create an output or display image showing the results of CCA or segmentation. Analyze particles has several useful outputs:

Count masks are very useful in combination with [Image > Look Up Tables > Glasbey]-style (IE random) LUTs

Applications: Colocalisation

Use cases, some simple guidance, JaCoP

Adapted from a slide by Fabrice Cordelieres

For more rigor, see: Aaron et al. J Cell Sci (2018) 131 (3): jcs211847., Figure 4

Colocalisation is highly dependent upon resolution! Example:

Same idea goes for cells. Keep in mind your imaging resolution!

We will walk through using JaCoP (Just Another CoLocalisation Plugin) to look at Pearson's and Manders' analysis
It's been revamped by the BIOP folks of EPFL: JaCoP-BIOP

The companion paper https://doi.org/10.1111/j.1365-2818.2006.01706.x

Pearson's Correlation Coefficient

Figure from https://doi.org/10.1111/j.1365-2818.2006.01706.x

Exercise: PCC

• Great for complete colocalisation
• Unsuitable if there is a lot of noise or partial colocalisation (see below)
• Midrange P-values (-0.5 to 0.5) do not allow reliable conclusions to be drawn
• Bleedthrough can be particularly problematic (as they will always correlate)

Manders' Overlap Coefficient

• Removes some of the intensity dependence of Pearson's and provides channel-specific overlap coefficients (M1 & M2)
• Values from 0 (no overlap) to 1 (complete overlap)
• Defined as "the ratio of the summed intensities of pixels from one channel for which the intensity in the second channel is above zero to the total intensity in the first channel"

Exercise: Manders'

Applications: Tracking

Correlating spatial and temporal phenomena, Feature detection, linkage, gotchas

Life exists in the fourth dimension. Tracking allows you to correlate spatial and temporal properties.

Most partcles look the same! Without any way to identify them, tracking is probabilistic.

Tracking has two parts: Feature Identification and Feature Linking

For every frame, features are detected, typically using a Gaussian-based method (eg. Laplacian of Gaussian: LoG)

Spots can be localised to sub-pixel resolution!

Without sub-pixel localisation, the precision of detection is limited to whole pixel values.

Feature linkage

For each feature, all possible links in the next frame are calculated. This includes the spot disappearing completely.

A 'cost matrix' is formed to compare the 'cost' of each linkage. This is globally optimised to calculate the lowest cost for all linkages.

In the simplest form, a cost matrix will usually consider distance. Many other parameters can be used such as:

• Intensity
• Shape
• Quality of fit
• Speed
• Motion type

Which can allow for a more accurate linkage especially in crowded or low S/N environments

Exercise: Tracking

Open 10-tracks.tif
Hit the arrow to play the movie. Right Click on the arrow to set playback speed

If you're interested in how the dataset was made see this snippet

Run [Plugins > Tracking > Trackmate]

If it's missing, download the TrackMate plugin and install it using [Plugins > Install...]

You should aim to set the minimum threshold that removes all noise.
Slide the navigation bar, then hit Preview to check out a few other timepoints. When satisfied press Next and advance the slides!

TrackMate will process the stacks.
Once it's done, hit next, accepting defaults until you reach 'Select a tracker'

Ensure `Simple LAP tracker` is selected and hit Next, then advance the slides!

Press Next and after processing, you should have tracks!

Press Next to get to outputs from Trackmate: (1) Tracking data

Press Next to get to outputs from Trackmate: (2) Movies!

You may want adjust the Display Options to get the tracks drawing the way you want (e.g. try "Local, Backwards")

While simple, Tracking is not to be taken on lightly!

• For the best results make sure the inter-particle distance is greater than the frame-to-frame movement. If not, try to increase resolution (more pixels) or decrease interval (more frames)
• The search radius increases processing time with HUGE datasets but in most case, has little effect on processing time. Remember that closer particles will still be linked preferably if possible.
• Keep it simple! Unless you have problems with noise, blinking, focal shifts and similar, do not introduce gap closing as this may lead to false-linkages
• 'Simple LAP tracker' does not include merge/spliting events, however Trackmate ships with the more complex 'LAP Tracker' which can handle merge/splitting events (but keep in mind your system!)
• Quality control! Look at your output carefully and make sure you're not getting 'jumps' where one particle is linked to another incorrectly

Applications: Stitching

Resolution vs field size, stitching, using overlaps, issues and bugs

Increasing resolution (via higher NA lenses) almost always leads to a reduced field

Often you will want both!

We can achieve this with tile scanning (IE. imaging multiple adjacent fields)

Stitching is the method used to put them back together again.
We'll use the Grid/Collection Stitching plugin

Exercise: No-overlap Stitching

Why do the images not line up?

Exercise: Stitching with Overlap

• Open Stitching_Overlap.zip. Unzip to the desktop.
• Run [Plugins > Stitching > Grid/Collection Stitching] again
• Use the same Grid settings (they should have been restored).
• Set `Tile overlap` to `10%`
• Update the Directory to the new path "Stitching_Overlap"
• File Names for tiles: overlap_{iii}.tif
• Ensure `Compute overlap` is checkeds
• Hit OK

The most important point is to know your data!

• Grid layout (dimensions and order!)
• Overlap
• Calibration

Applications: Batch Processing

Why batch process? File conversion, batch processing, scripting

Manual analysis (while sometimes necessary) can be laborious, error prone and not provide the provenance required. Batch processing allows the same processing to be run on multiple images.

The built-in [Process > Batch] menu has lots of useful functions:

We'll use a subset of dataset BBBC008 from the Broad Bioimage Benchmark Collection

• Download the zip file from here to the desktop
• Unzip (right click and "Extract All") to end up with a folder on your desktop called BBBC008_partial

• Make another folder on the desktop called Output

Exercise 7: Batch Convert

• Ensure you have the the zip file BBBC008_partial.zip extracted to a known location and a that you have a second folder called "Output" next to it.
• Open Task7.pdf and follow the instructions there.

• With the Recorder open, (almost) every action you take in Fiji will be recorded as a macro command--this includes many Plugin functions!
• The recorded Macros can be saved ("Create" button and Save) and then re-run [Plugins > Macros > Run...]

Exercise 8: Macro Recorder

• Ensure you have the the zip file BBBC008_partial.zip extracted to a known location and a that you have a second folder called "Output" next to it.
• Open Task8.pdf and follow the instructions there.

Scripting

A very brief foray into scripts

Scripting is useful for running the same process multiple times or having a record of how images were processed to get a particular output

Fiji supports many scripting languages including Java, Python, Scala, Ruby, Clojure and Groovy through the script editor which also recognises the macro language from the previous example (which we'll be using)

As an example, we're going to (manually) create a montage from a three channel image, then see what the script looks like

• Open 06-MultiChannel.tif
• Open the macro recorder: [Plugins > Macros > Record]

Got it? Have a look at the Macro Recorder and see if you can see the commands you ran

Open the script editor with [File > New > Script] and copy in the following code:

	//-- Record the filename
	imageName=getTitle();
	print("Processing: "+imageName);
	//-- Display the stack in greyscale, create an RGB version, rename
	Property.set("CompositeProjection", "null");
	Stack.setDisplayMode("grayscale");
	run("RGB Color");
	rename("channels");
	//-- Select the original image
	selectWindow(imageName);
	//-- Display the stack in composite, create an RGB version, rename
	Property.set("CompositeProjection", "Sum");
	Stack.setDisplayMode("composite");
	run("RGB Color");
	rename("merge");
	//-- Close the original
	close(imageName);
	//-- Put the two RGB images together
	run("Concatenate...", "  title=newStack open image1=channels image2=merge");
	//-- Create a montage
	run("Make Montage...", "columns=4 rows=1 scale=0.50 border=3");
	//-- Close the stack (from concatenation)
	close("newStack");
	

Open 06-MultiChannel.tif again and hit Run

Comments, variables, print, active window

This script operates on an open image but it's easily converted to a batch processing script using the built in templates:

The full script is here. I added these lines at the top and bottom:

open(input + File.separator + file);

saveAs("png", output + File.separator + replace(file, suffix, ".png"));
	close("*");
	

We'll go into more detail on scripts in the future

In the meantime:

• ImageJ language reference
• ImageJ function reference
• Getting started with Version Control for scripting

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