Lookup tables
Prerequisites
Before starting this lesson, you should be familiar with:
Learning Objectives
After completing this lesson, learners should be able to:
Understand how the numerical values in an image are transformed into colourful images.
Understand what a lookup table (LUT) is and how to adjust it.
Appreciate that choosing the correct LUT is a very serious responsibility when preparing images for a talk or publication.
Motivation
Images are a collection of a lot (millions) of values, which is information that is hard to process for our human brains. Thus, one typically assigns a color to each distinct value, by means of a lookup table (LUT). There is no fix recipe for how to adjust this mapping from numbers to colors. It is easy to chose a mapping that hides certain information in an image, while emphasising other information. Thus, configuring this mapping properly is a great responsibility that scientists have to take on when presenting their image data.
Concept map
Figure
Activities
Explore LUTs
- Open the image xy_8bit__nuclei_high_dynamic_range.tif
- Explore different contrast settings
- Observe that there are very dim nuclei
- Observe that LUT settings do not change pixel values
- Explore various single color LUTs (e.g., gray, green, red, blue)
- Understand that gray is the recommended default
- Understand that certain LUTs such as red and blue should be avoided
- Explore various multi color LUTs, which can be helpful to
- highlight extreme values
- render high dynamic range data without “clipping information”
- Visualise the LUT itself, e.g. as an inset in the image
- Understand that this is especially important for multi-color LUTs where the mapping from the displayed color to the numeric data is not obvious
Show activity for:
ImageJ GUI
- Open an image
- Change the contrast settings
- [ Image › Adjust › Brightness/Contrast… ]
- Explore different min and max values
- Appreciate that at certain settings a very dim nucleus becomes visible
- Check that the pixel values did not change
- Never click [ Apply ]
- Explore various single color LUTs, e.g.
- [ Image › Lookup Tables › Green ]
- [ Image › Lookup Tables › Blue ]
- [ Image › Lookup Tables › Red ]
- Avoid red! 15% of males cannot see anything here!
- Magenta can be a better alternative.
- Explore various multi color LUTs, e.g.
- [ Image › Lookup Tables › Fire ]
- Good for this high-dynamic range image
- [ Image › Lookup Tables › HiLo ]
- Good to see extreme values
- Show the LUT
- Especially useful for multi-color LUTs
- [ Analyze › Tools › Calibration Bar… ]
- Explore the various settings
ImageJ Macro
skimage napari
Galaxy Napari
- Upload an image to Galaxy
- Go to https://usegalaxy.eu
- In the Tools panel on the left, click
Upload Data
- Click
Paste/Fetch data
button- Paste the image url: https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_high_dynamic_range.tif and click the
Start
button- Click the
Close
button after upload finishes, then the image will be available in your Galaxy history.- Start the Napari Interactive Tool
- In the Tools panel on the left, search for
Run Napari interactive tool
- Select
xy_8bit_nuclei_high_dynamic_range.tif
from theImages
dropdown list.- Click the
Run Tool
button. Once theOpen
link appears at the top of the page, click it to open Napari in a separate browser tab.- In the Napari browser tab, navigate to
File -> Open File(s)
and select the imagexy_8bit_nuclei_high_dynamic_range.tif
from theinput
folder.- Change the Contrast settings
- Experiment with different minimum and maximum values of the
contract limits
.- Notice how, at certain settings, a very dim nucleus becomes visible.
- Explore different LUTs, e.g.
- Go to
File › Open File(s)
- Select the same image
xy_8bit_nuclei_high_dynamic_range.tif
from theinput
folder. A new layer will appear in the bottom left pane.- Change the
colormap
toturbo
, from the layer options in the top left pane.- Turn on grid mode by clicking the
Grid
button located at the bottom left,second from the right.
Display several images with same LUT settings
Display image sets with the same gray scale LUT and the same contrast settings. Visualise the LUT as an inset in both images (you may also attempt to visualise the LUT only once outside the images). This is what one typically should do for a presentation or publication for data that were acquired with the same microscope settings.
Example data
- Collagen secretion
- Nuclear protein expression
Show activity for:
ImageJ GUI
- Open one of the above pairs of images
- Choose a suitable LUT using
Image › Lookup Tables › ...
- Adjust brightness and contrast in one image using
Image › Adjust › Brightness/Contrast...
- To avoid intensity clipping one typically sets the contrast on the brightest image (this may depend on your scientific question though…)
- To find out which image is brighter you can try to use
Analyze > Histogram
- Use the
Set
button inImage › Adjust › Brightness/Contrast...
and check[X] Propagate to all other open images
- Visualise the current LUT as an inset in both images using
Analyze › Tools › Calibration Bar...
- Export the image using
Plugins › BioVoxxel Figure Tools › Export SVG
(requires BioVoxxel update site)
- SVG preserves the rendering of the scale bar at different zoom levels.
ImageJ Macro
skimage napari
Galaxy Napari
- Upload one of the above pairs of images to Galaxy
- Go to https://usegalaxy.eu
- In the Tools panel on the left, click
Upload Data
- Click
Paste/Fetch data
button- Paste the URLs of the two images(one line per URL) and click the
Start
button- Click the
Close
button after upload finishes, then the image will be available in your Galaxy history.- Start the Napari interactive tool
- In the Tools panel on the left, search for
Run Napari interactive tool
- Select the two uploaded images from the
Images
dropdown list- Click the
Run Tool
button. Once theOpen
link appears at the top of the page, click it to open Napari in a separate browser tab.- In the Napari tab, navigate to
File -> Open file(s)
, and select the two images from theinput
folder.- Turn on grid mode by clicking the
Grid
button located at the bottom left,second from the right. The two images will appear side by side- Adjust the
contrast limits
and apply the same values to both images to compare them directly
Assessment
Compute how the contrast limits affect the rendered pixel brightness
Read the below section “Explanations: Single color lookup tables” and use the formula that is given there to compute the rendered pixel brightness for the following scenarios:
value = 49, min = 10, max = 50, brightness = ?
value = 100, min = 0, max = 65, brightness = ?
value = 10, min = 20, max = 65, brightness = ?
Solution
0.975
1.538 -> 1.0
-0.22 -> 0.0
Fill in the blanks
Fill in the blanks using those words: larger than, smaller than
- Pixels with values _____
max
will appear saturated. - Pixels with values _____ the
min
will appear black (using a single color LUT).
Solution
- larger than
- smaller than
Key points
A LUT has configurable contrast limits that determine the pixel value range that is rendered linearly.
LUT settings must be responsibly chosen to convey the intended scientific message and not to hide relevant information.
A gray scale LUT is usually preferable over a colour LUT, especially blue and red are not well visible for many people.
For high dynamic range images multi-color LUTs may be useful to visualise a wider range of pixel values.
Explanations
Lookup tables do the mapping from a numeric pixel value to a color. This is the main mechanism how we visualise microscopy image data. In case of doubt, it is always a good idea to show the mapping as an inset in the image (or next to the image).
Single color lookup tables
Single color lookup tables are typically configured by chosing one color such as, e.g., grey or green, and choosing a min
and max
value that determine the brightness of this color depending on the value
of the respective pixel in the following way:
brightness( value ) = ( value - min ) / ( max - min )
In this formula, 1 corresponds to the maximal brightness and 0 corresponds to the minimal brightness that, e.g., your computer monitor can produce.
Depending on the values of value
, min
and max
it can be that the formula yields values that are less than 0 or larger than 1.
This is handled by assinging a brightness of 0 even if the formula yields values < 0 and assigning a brightness of 1 even if the formula yields values
larger than 1
. In such cases one speaks of “clipping”, because one looses (“clips”) information about the pixel value (see below for an example).
Clipping example
min = 20, max = 100, v1 = 100, v2 = 200
brightness( v1 ) = ( 100 - 20 ) / ( 100 - 20 ) = 1
brightness( v2 ) = ( 200 - 20 ) / ( 100 - 20 ) = 2.25
Both pixel values will be painted with the same brightness as a brightness larger than 1
is not possible (see above).
Multi color lookup tables
As the name suggestes multi color lookup tables map pixel gray values to different colors.
For example:
0 -> black
1 -> green
2 -> blue
3 -> ...
Typical use cases for multi color LUTs are images of a high dynamic range (large differences in gray values) and label mask images (where the pixel values encode object IDs).
Sometimes, also multi color LUTs can be configured in terms of a min
and max
value. The reason is that multi colors LUTs only have a limited amount of colors, e.g. 256 different colors. For instance, if you have an image that contains a pixel with a value of 300 it is not immediately obvious which color it should get; the min
and max
settings allow you to configure how to map your larger value range into a limited amount of colors.
Follow-up material
Recommended follow-up modules:
Learn more: