  With the increasing size of datasets and demand for real time response for
interactive applications, improving runtime for algorithms with excessive
computational requirements has become increasingly important. Many different
algorithms combining efficient priority queues with various helper structures
have been proposed for computing grey-weighted distance transforms. Here we
compare the performance of popular competitive algorithms in different
scenarios to form practical guidelines easy to adopt. The label-setting
category of algorithms is shown to be the best choice for all scenarios. The
hierarchical heap with a pointer array to keep track of nodes on the heap is
shown to be the best choice as priority queue. However, if memory is a critical
issue, then the best choice is the Dial priority queue for integer valued costs
and the Untidy priority queue for real valued costs.
