Computing exact distance transforms is a common problem in image processing and computer vision.
This toolbox provides the generic framework for distance transform algorithms, and comes bundled with 2 fast quasi-exact approximation methods based on chamfer mask propagation (one is based on a 3x3x3 neighborhood mask, the other on a 5x5x5 neighborhood mask). These chamfer algorithms just need to sweep through the image data twice to compute the distance map, which makes them very fast and efficient, while yielding only minor errors compared to absolute Euclidean distance.
Adding new algorithms
The chamfer algorithms are very fast and efficient, although they may not be of sufficient precision in some very specific applications. You might also want to work with larger chamfer sizes that aren't currently available. In both cases you are very welcome to implement your own algorithms and integrate them seemlessly with this one, simply by creating a new plug-in in which you extend the abstract class "DistanceTransform" with your own code. Once installed, this new plug-in will be automatically recognised and added it to the list of algorithms available in the graphical user interface (both in standalone and Protocols mode).