Parallel N-Dimensional Exact Signed Euclidean Distance Transform

Abstract

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 a three-dimensional signed parallel implementation of the exact Euclidean distance transform algorithm developed by Maurer et al. 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.

Toward Real-Time Image Guided Neurosurgery Using Distributed and Grid Computing

“…This Chapter presented only a few of the most important findings and results. However, several efforts at CRTC alone address other aspects like: (1) robust and real-time image-to-mesh (I2M) conversion [44,92,64,65,42,21,43,47,48,59,77,8,15,16,51,50,53,52,54,78,17,55,56,49,57,29,58,31,46,29,30,60], (2) more accurate computation of Hausdorf Distance [13,41], (3) parallel Euclidean Distance Transform, which is critical for parallel I2M conversion [98], and many preliminary efforts that lead to the findings presented in this Chapter and we list them here for completeness without expanding on them [6,45,22,20,74,75,104,76,83,72,81,…”