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Quality meshing of 2D images with guarantees derived by a computer-assisted proof

 

Jing Xu and Andrey Chernikov.

 

Published in VMASC 2014 Capstone Conference, Best paper award in the Medical Simulation track., Suffolk, VA, April, 2014

 

Abstract

 

Computing the Euclidean Distance Transform (EDT) for binary images is an important problem with applications involving medical image processing, computer vision, computational geometry, and pattern recognition. Currently, there exists a sequential algorithm of O(n) complexity developed by Maurer et al. and a parallel implementation of Maurer's algorithm developed by Staubs et al. with a theoretical complexity of O(n/p) for n voxels and p threads. In this paper, we present an efficient, scalable parallel implementation of Maurer's algorithm for large datasets with high efficiency for 16 processors.