Parallel N-Dimensional Exact Signed Euclidean Distance Transform

The computation speed for distance transforms becomes important in a wide variety of image processing applications. Current ITK library filters do not see any benefit from a multithreading environment. We introduce an N-dimensional signed parallel implementation of the exact Euclidean distance transform algorithm developed by Maurer et al.[1] with a theoretical complexity of O(n/p) for n voxels and p threads. Through this parallelization and efficient use of data structures we obtain approximately 3 times mean speedup on standard tests on a 4-processor machine compared with the current ITK exact Euclidean distance transform filter[4].