MRI Image Segmentation Based on a GPU Shortest Path Algorithm
- DOI
- 10.2991/icicci-15.2015.1How to use a DOI?
- Keywords
- Keywords-CUDA; shortest path algorithm; medical image segmentation
- Abstract
Abstract—Dijkstra algorithm can be adopted to distinguish different parts of boundaries in medical image segmentation problem, which can be a reference for further segmentation operation. However, classical Dijkstra algorithm can hardly adapt to real time image segmentation problem owing to its exponentially O(n2) computing complexity, especially for the increasing number of nodes. In this paper, we designed and implemented a parallel shortest path algorithm accelerated by GPU for medical image segmentation problem. A dynamic relax approach is presented to optimize classical Dijkstra algorithm. Therefore, the new parallel Dijkstra algorithm can be easily applied to CUDA parallel framework without concerning about GPU hardware and CUDA optimize details. Two experiments have been conducted to evaluate the algorithm performance. The results show that our new Dijkstra algorithm can get 8 speedup for 4096 points compared with the classical Dijkstra algorithm. Besides, an impressing result with two speed up for 128*128 points problems is demonstrated in parallel Dijkstra compared with parallel Moore. In conclusion, the new parallel Dijkstra algorithm can significantly improve the real-time performance of image segmentation.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Jie Wang AU - Weihao Chen PY - 2015/09 DA - 2015/09 TI - MRI Image Segmentation Based on a GPU Shortest Path Algorithm BT - Proceedings of the 2nd International Conference on Intelligent Computing and Cognitive Informatics PB - Atlantis Press SP - 1 EP - 4 SN - 1951-6851 UR - https://doi.org/10.2991/icicci-15.2015.1 DO - 10.2991/icicci-15.2015.1 ID - Wang2015/09 ER -