An Evaluation of Parallelization Techniques for MRF Image Segmentation

Markov Random Fields (MRFs) are of great interest to the medical image analysis community but suffer from high computational complexity and difficulties in parameter selection. For these reasons, efforts have been made to develop more efficient algorithms for solving MRF optimization problems in order to enable reduced run-times and better interactivity. However, these algorithms are often implemented in serial and thus do not benefit from multi-core technology. In this work, we demonstrate a parallelized implementation of a popular MRF optimization algorithm, belief propagation, and use it to perform a binary image segmentation. By utilizing modern, lightweight parallel-programming techniques we are able to achieve a speedup of approximately 8 times, reducing the average segmentation time of a single 600×450 image from 12.7s to 1.6s.