10834

Computer Tomography and Ultrasonography Image Registration Based on the Cooperation of GPU and CPU

Ying-Chih Lin, Chien-Liang Huang, Chin-Sheng Chen, Wen-Chung Chang, Yu-Jen Chen, Chia-Yuan Liu
Graduate Institute of Automation Technology, National Taipei University of Technology
Journal of Signal and Information Processing, 4, 80-85, 2013
@article{ying2013computer,

   title={Computer Tomography and Ultrasonography Image Registration Based on the Cooperation of GPU and CPU},

   author={Ying-Chih, Lin and Chien-Liang, Huang and Chin-Sheng, Chen and Wen-Chung, Chang and Yu-Jen, Chen and Chia-Yuan, Liu},

   journal={Journal of Signal and Information Processing},

   volume={4},

   pages={80},

   year={2013},

   publisher={Scientific Research Publishing}

}

Download Download (PDF)   View View   Source Source   

557

views

Image registration is wildly used in the biomedical image, but there are too many textures and noises in the biomedical image to get a precise image registration. In order to get the excellent registration performance, it needs more complex image processing, and it will spend expensive computation cost. For the real time issue, this paper proposes edge gradient direction image registration applied to Computer Tomography (CT) image and Ultrasonography (US) image based on the cooperation of Graphic Processor Unit (GPU) and Central Processor Unit (CPU). GPU can significantly reduce the computation time. First, the CT image slice is extracted from the CT volume by the region growing and the interpolation algorithm. Secondly, the image pre-processing is employed to reduce the image noises and enhance the image features. There are two kinds of the image pre-processing algorithms invoked in this paper: 1) median filtering and 2) anisotropic diffusion. Last but not least, the image edge gradient information is obtained by Canny operator, and the similarity measurement based on gradient direction is employed to evaluate the similarity between the CT and the US images. The experimental results show that the proposed architecture can distinctively improve the efficiency and are more suitably applied to the real world.
VN:F [1.9.22_1171]
Rating: 0.0/5 (0 votes cast)

* * *

* * *

Follow us on Twitter

HGPU group

1855 peoples are following HGPU @twitter

Like us on Facebook

HGPU group

405 people like HGPU on Facebook

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: