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
graph TD
V("Image pixel value") --> L("Lookup table (LUT)")
L --> |does not change|V
L --> |changes|C("Displayed pixel color & brightness")
Figure
Left: Image displayed with a grey LUT and the color mapping as an inset. Right: Image shown with several different LUTs.
# %% [markdown]
# # Explore LUTs
# %%
# Instantiate the napari viewer
importnapariviewer=napari.Viewer()# %%
# Read the image
fromOpenIJTIFFimportopen_ij_tiffimage,axes,scales,units=open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_8bit__nuclei_high_dynamic_range.tif')# %% [markdown]
# ### Napari GUI alternative to load data
# Drag and drop and rename the layer (alternative for loading data)\
# Change name of layer `viewer.layers[0].name = 'image_grayscale'` \
# Get the data as numpy array `image = viewer.layers['image_grayscale'].data`
# %%
# Check image type and values
importnumpyasnpprint(image.dtype,np.min(image),np.max(image))# %%
# View the intensity image as grayscale
viewer.add_image(image,name='image_grayscale',colormap='gray')# %%
# Change brightness and contrast
viewer.layers['image_grayscale'].contrast_limits=(100,175)# %% [markdown]
# **Napari GUI** explore different contrast limits
# %%
# View the intensity image as grayscale
viewer.add_image(image,name='image_grayscale2',colormap='gray')# %% [markdown]
# **Napari GUI** visualize images side by side\
# **Napari GUI** change brightness and contrast to visualize dim nuclei
# %%
# Check available colormap
print(list(napari.utils.colormaps.AVAILABLE_COLORMAPS))# %%
# Change colormap
viewer.add_image(image,name='image_turbo',colormap='turbo')# %% [markdown]
# **Napari GUI** explore different LUTs
# %%
# Extract image data from the layers
image_grayscale=viewer.layers['image_grayscale'].dataimage_grayscale2=viewer.layers['image_grayscale2'].data# %%
# Compare raw data
print(image_grayscale[0:5,0:5])print(image_grayscale2[0:5,0:5])print((image_grayscale==image_grayscale2).all())
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.
# %% [markdown]
# # Configure LUTs
# %%
# Instantiate the napari viewer
importnapariviewer=napari.Viewer()# %%
# Read the image
fromOpenIJTIFFimportopen_ij_tiffimage_control,axes_control,scales_control,units_control=open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_calibrated_16bit__nuclear_protein_control.tif')image_treated,axes_treated,scales_treated,units_treated=open_ij_tiff('https://github.com/NEUBIAS/training-resources/raw/master/image_data/xy_calibrated_16bit__nuclear_protein_treated.tif')# %%
# View the intensity image as grayscale
viewer.add_image(image_control,name='control',colormap='gray')viewer.add_image(image_treated,name='treated',colormap='gray')# %% [markdown]
# **Napari GUI** Show images side by side \
# **Napari GUI** Inspect possible consistent limits for both images
# %%
# Apply limits to both images
viewer.layers['control'].contrast_limits=(0,1024)viewer.layers['treated'].contrast_limits=(0,1024)
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).
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.
