Cutting Display of Industrial CT Volume Data Based on GPU

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Abstract:

The rapid development of Graphic Processor Units (GPU) in recent years in terms of performance and programmability has attracted the attention of those seeking to leverage alternative architectures for better performance than that which commodity CPU can provide. This paper presents a new algorithm for cutting display of computed tomography volume data on the GPU. We first introduce the programming model of the GPU and outline the implementation of techniques for oblique plane cutting display of volume data on both the CPU and GPU. We compare the approaches and present performance results for both the CPU and GPU. The results show that cutting display image generated by GPU algorithm is clear, frame rate on GPU is 2-9 times than that on CPU.

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Periodical:

Advanced Materials Research (Volumes 271-273)

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1096-1102

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Online since:

July 2011

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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[1] Z. Sh. Tang: Visualization of 3D data field. Beijing, Tsinghua University Press, (1999).

Google Scholar

[2] R.A. Robb: 3-D Visualization in Biomedical Applications, Proceedings of Annual Reviews Biomedical Engineering, Rochester, Minnesota, USA, pp.377-399, (1999).

Google Scholar

[3] Y.P. Lu, J. Wang, R. Liu: Study on reslicing method for volume data from industrial CT , Computer Engineering and Applications, Vol. 43(22), pp.201-203, (2007).

Google Scholar

[4] A.D. Zhang, J. Li,L.X. Sun: Three dimensional reconstruction of continuous ICT images by MATLAB , Nuclear Electronics & Detection Technology, Vol. 26(4), pp.489-491, (2006).

Google Scholar

[5] Y. Frishman: Multi-Level Graph Layout on the GPU, IEEE Transactions on Visualization and Computer Graphics, Vol. 13( 6), pp.1310-1317, (2007).

DOI: 10.1109/tvcg.2007.70580

Google Scholar

[6] P. Kehoe and A.F. Smeaton: Using Graphics Processor Units (GPUs) for Automatic Video Structuring, Eight International Workshop on Image Analysis for Multimedia Interactive Services(WIAMIS'07), ( 2007).

DOI: 10.1109/wiamis.2007.85

Google Scholar

[7] H. Takizawa: Radiative Heat Transfer Simulation Using Programmable Graphics Hardware, Proceedings of the 5th IEEE/ACIS International Conference on Computer and Information Science and 1st IEEE/ACIS, ( 2006).

DOI: 10.1109/icis-comsar.2006.70

Google Scholar

[8] O. Fialka, M. Cadik: FFT and Convolution Performance in Image Filtering on GPU, Proceedings of the Information Visualization (IV'06), (2006).

DOI: 10.1109/iv.2006.53

Google Scholar

[9] R. Fernando, M.J. Kilgard: The Cg Tutorial: The Definitive Guide to Programmable Real-Time Graphics, Addison-Wesley, (2003).

Google Scholar

[10] D. Shreiner, M. Woo, J. Neider, T. Davis: OpenGL Programming Guide: The Official Guide to learning OpenGl, Version 2, Fifth Edition, Addison-Wesley Press, (2005).

Google Scholar