Drop2: New version now available on Github!

What's it about?

In 2006, we developed a new approach for image registration and motion estimation based on Markov Random Fields. On this website, you can download our software and test it for your own research and applications. From time to time, we will provide an updated version of the software including latest developments and/or new features.

Related Publication

Deformable Medical Image Registration: Setting the State of the Art with Discrete Methods

Authors: Ben Glocker, Aristeidis Sotiras, Nikos Komodakis, Nikos Paragios

This review discusses a novel deformable image registration paradigm that exploits Markov random field formulation and powerful discrete optimization algorithms. We express deformable registration as aminimal cost graph problem, where nodes correspond to the deformation grid, a node's connectivity corresponds to regularization constraints, and labels correspond to 3D deformations. To cope with both iconic and geometric (landmark-based) registration, we introduce two graphical models, one for each subproblem. The two graphs share interconnected variables, leading to a modular, powerful, and flexible formulation that can account for arbitrary image-matching criteria, various local deformation models, and regularization constraints. To cope with the corresponding optimization problem, we adopt two optimization strategies: a computationally efficient one and a tight relaxation alternative. Promising results demonstrate the potential of this approach. Discrete methods are an important new trend in medical image registration, as they provide several improvements over the more traditional continuous methods. This is illustrated with several key examples where the presented framework outperforms existing general-purpose registration methods in terms of both performance and computational complexity. Our methods become of particular interest in applications where computation time is a critical issue, as in intraoperative imaging, or where the huge variation in data demands complex and application-specific matching criteria, as in large-scale multimodal population studies.

Published in: Annual Review of Biomedical Engineering, Vol. 12, 2011, pp. 219-244


Release - 01/06/2011
Please check out the latest version of our software.

FastPD Optimizer
A stand-alone version of the discrete optimization method is available at the following site: FastPD Code

Tutorial at MICCAI 2010

We have co-organized a tutorial on intensity-based deformable image registration at MICCAI2010 in Beijing. Check out the website for tutorial materials!