Implementation of medical image segmentation in CUDA
School of software, Shanghai Jiaotong University, Shanghai, P.R. China
International Conference on Information Technology and Applications in Biomedicine, 2008. ITAB 2008
@inproceedings{pan2008implementation,
title={Implementation of medical image segmentation in CUDA},
author={Pan, L. and Gu, L. and Xu, J.},
booktitle={Information Technology and Applications in Biomedicine, 2008. ITAB 2008. International Conference on},
pages={82–85},
year={2008},
organization={IEEE}
}
As the fast development of GPU, people tend to use it for more general purposes than its original graphic related work. The high parallel computation capabilities of GPU are welcomed by programmers who work at medical image processing which always have to deal with a large scale of voxel computation. The birth of NVIDIAreg CUDAtrade technology and CUDA-enabled GPUs brought a revolution in the general purpose GPU area. In this paper, we propose the implementation of several medical image segmentation algorithms using CUDA and CUDA-enabled GPUs, compare their performance and results to the previous implementation in old version of GPU and CPU, indicate the advantages of using CUDA technology and how to design algorithm to make full use of it.
May 31, 2011 by hgpu
