Exploring self-similarity for multimodal registration

Image registration is a challenging task in medical imaging analyses.

The goal of the registration process: integrate the information obtained from different sources to gain more complex and detailed image.

In our project we explored the possibilities for the expansion of the MIND descriptor using previous work done using the image’s gradient.
Our project dealt with multi-model medical image registration.
Medical imaging can be performed in various ways each producing vastly different image. As a result the need of model independent registration has risen.

In a previous work, the MIND registration algorithm was examined and its results for MRI-CT registration looked promising. However, it was claimed that MIND performs poorly on edges therefore an improvement was made taking into account the image's gradient (the GMIND algorithm).
The new algorithm performed better on edges but worse overall.

Exploring self-similarity for multimodal registration

 

Exploring self-similarity for multimodal registration
Exploring self-similarity for multimodal registration
Collaboration:

Rappaport’s Multi-Disciplinary Laboratories

Unit at Rambam Hospital