Skip to content
ImageJ examples for the 2019 DAIS Learnathon.
Java
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
resources
src/main/java
.gitignore
LICENSE
README.md
pom.xml

README.md

transforms_tutorial

Imglib2 / ImageJ examples for the 2019 DAIS Learnathon. See also the 2019 learnathon github repo

Sample data

Sample data for the Point rendering example can be downloaded here. Extract the contents of the zip file into this repositories resources folder.

Overview

These examples show how to apply spatial transformations to points and images using imglib2.

Spatial transformations

Spatial transformations change an image by "moving the image" rather than affecting pixel values. A transform is a function with a point coordinate as an input, and another point coordinate as an output.

Discrete transformations

Discrete transformations have discrete valued inputs and outputs. In compute-science terms, this means that the point coordinates are stored as integer (int/long) valued variables. We will call this set of discrete location the "image grid". Discrete transforms can not accept points off the image grid, nor can they produce

Continuous ("real") transformations

Continuous transformations have continous valued inputs and outputs. In math language, the point coordinates are Real numbers. In computer-science language, the point coordinates are represented by floating-point (float/double) variables. Continuous transformations can accept any points as inputs or outputs (on or between the image grid).

The function:

f(x,y) = ((x + 100), ( y - 0.5 ))

is a continuous transformation that translates the point (x,y) 100 units in the +x direction, and -0.5 units in the +y direction.

g(i,j) = ((j), (-i))

is a discrete transformation that:

  1. Inverts the i axis
  2. Changes the roles of the i- and j-axes.

Example 1 - Discrete and continuous transforms in imglib2

This example shows how to:

  1. Apply a simple translation to an image and display it
  2. Apply a complicated discrete transformation to an image and show it
  3. Highlights some common pitfalls.

Exercise: Use what we learned in these examples to resample an image (preserving the field of view).

Example 2 - Composing transforms

Composition transformation is just like composing functions - the input of one transformation is the output of another.

See this Robert Haase tweet and the related discussion.

This example we will learn:

  1. That it is important to interpolate as little as possible.
  2. Why that is the case.
  3. That transformation composition is the solution.

Example 3 - Displacement ("deformation") fields

Deformation fields are one of the most common representation of non-linear spatial transformations.

This example we will learn:

  1. What a displacement field is.
  2. How to construct a deformation field and warp an image with it.
  3. How to numerically invert a displacement field.
  4. Adding deformation fields is not the same as composing them.

Point rendering example

This example reiterates some of the topics we've learned about in other examples with a more realistic example. Before starting, make sure you've downloaded this sample data and extracted the contents of the zip file into this repositories resources folder.

This example shows us how to:

  1. Efficiently render point coordinates a "blob image"
  2. Observe the differences between the images produced if one:
    • transforms points then renders
    • renders the image, then transforms it
  3. Apply a real, deformable transformation between EM and a light-level template to point coordinates derived by predicting synapse locations from EM.
    • over the whole brain
    • over a subset of the brain

The light image data comes from An unbiased template of the Drosophila brain and ventral nerve cord. The EM image data come from the Complete Electron Microscopy Volume of the Brainof Adult Drosophila melanogaster ("FAFB") image data available here, and the synapse predictions from those data are from Synaptic Cleft Segmentation in Non-Isotropic Volume Electron Microscopy of the Complete Drosophila Brain

Tips for a joyful transformation experience

  • Save your transforms to disk when transforming images.
    • It's more important to save the transforms, than to save the images(!)
  • Use and respect the image metadata
    • Set pixel spacing and origin in physical units
  • Define transforms in physical units (nm, um, mm, au, whatever) not pixels.
    • This makes your life super easy when dealing with images of the same thing taken at different resolutions and/or with slightly different fields of view.
  • Respect your origin
    • Pick it, and stick with it.
    • Don't define rotations about the center because it's convenient (now), it will cause pain (later).

Related topics in these examples

You can’t perform that action at this time.