Abstrakt
B-Spline based image registration has gained significant attention in the last decade especially in the field of medical image processing. Despite the good properties of B-Splines in this setting such as locality, low number of parameters and scalability there is still room for improvement in speed if we want to employ the method in a more complicated data processing pipeline.
We can dramatically improve performance by smarter computation of integrals inside the fitting process. We found out that in some cases we can improve convergence rate in orders of magnitude using Monte Carlo with importance sampling.
But in the case of euclidean distance map based criterion importance sampling may fail. Random Monte Carlo works well and also Quasi Monte Carlo based integration gives reasonable results.
The choice of sampling strategy always depends on a chosen criterion and data at hand.