Image Registration Workshop

Helmholtz Imaging is here for you with support units at 3 centers

Image by Cmglee, license: CC BY-SA 4.0
Link to interpolation weights and examples notebook: example_notebooks/3_interpolation.ipynb

• When down-sampling an image, aliasing artifacts can occur, thus applying anti-alising filters can help to reduce these artifacts.
• But, anti-aliasing filters can also blur the image, so it’s a trade-off between sharpness and aliasing artifacts.

• Intensity-Based Registration
  • Iterative process that optimizes aligned pixel intensity similarities (e.g. correlation coefficient or MSE).

• Mutual Information-Based Registration
  • Iteratively aligns multimodal images by maximizing the statistical relationship between them.

• Frequency Domain Methods
  • Transforms images into the Fourier space to compute alignment transformations.

• Looking at the equation, the more structure we have in the joint histogram, the lower its entropy, and thus the mutual information is higher.

• Mutual Information implementation notebook: example_notebooks/mutual_information.ipynb

• Elastix / ITKElastix is a powerful open-source tool for intensity-based image registration, widely used in medical imaging and other fields.
  • It provides a flexible framework for various registration tasks, supporting multiple transformation models and similarity metrics.
  • ITKElastix has a Python interface. ITK + Elastix are C++ native.
  • ITKElastix toy example: example_notebooks/6_itkelastix_toy_tutorial.ipynb
• ANTs is another popular tool (ITK based) for intensity-based registration, known for its advanced algorithms and versatility in handling different image modalities.
  • It’s especially useful for 3D and elastic deformation registration tasks.
  • Originally developed for neuroimaging applications but applicable to other domains as well.
  • Python / R / command line interface.

• ITKElastix link: github.com/InsightSoftwareConsortium/ITKElastix
• ANTs link: github.com/ANTsX/ANTs

two main steps:

1. Detecting and matching similarities: identifying corresponding regions or features
   • Feature-Based Registration (SIFT, SURT, ORB, BRISK, FAST)
   • Segmentation-Based Registration
   • Model Fitting
   • Graph-Based Methods
2. Estimating and applying transformations: Finding and applying the optimal transformation

1. Detecting Similarities:
   • Feature Detection: Detect keypoints and their descriptors (e.g. using SIFT)
   • Feature Matching: Match features between images to select keypoints to use.
2. Estimating and Applying Transformations: one image is transformed in space to match the other
   • Transformation Estimation: Compute transformation matrix (e.g. affine) using matched keypoints.
   • Warping: Apply transformation to align images.

• SIFT based registration notebook: example_notebooks/sift_example.ipynb

• unlike some other vision tasks, deep learning has not completely supplanted classical methods for registration.
• However, deep learning based methods can learn complex patterns and deformations, making them suitable for challenging registration tasks and shine in speed and in leveraging training data.

VoxelMorph

Thanks for participating. Please feel free to reach out with any questions.

Contact:     ella.bahry@mdc-berlin.de    support@helmholtz-imaging.de

Workshop available on: github.com/ida-mdc/registration-workshop

Thanks to Deborah Schmidt for the template!