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CPU-based real-time maximim intensity projection via fast matrix transposition using parallelization operations with AVX instruction set

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A Correction to this article was published on 31 October 2017

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Abstract

Rapid visualization is essential for maximum intensity projection (MIP) rendering, since the acquisition of a perceptual depth can require frequent changes of a viewing direction. In this paper, we propose a CPU-based real-time MIP method that uses parallelization operations with the AVX instruction set. We improve shear-warp based MIP rendering by resolving the bottle-neck problems of the previous method of a matrix transposition. We propose a novel matrix transposition method using the AVX instruction set to minimize bottle-neck problems. Experimental results show that the speed of MIP rendering on general CPU is faster than 20 frame-per-second (fps) for a 512 × 512 × 552 volume dataset. Our matrix transposition method can be applied to other image processing algorithms for faster processing.

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  • 31 October 2017

    The authors regret that acknowledgment of the financial support of the first author was omitted from the manuscript.

References

  1. Belina S, Cuk V, Klapan I (2009) Virtual endoscopy and 3D volume rendering in the management of frontal sinus fractures. Coll Antropol 33:43–51

    Google Scholar 

  2. Chen K, Duan Y, Yan L, Sun J, Guo Z (2012) Efficient SIMD optimization of HEVC encoder over X86 processors. Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), pp 1–4

  3. Cui J, Liu Y, Xu Y, Zhao H, Zha H (2013) Tracking generic human motion via fusion of low- and high-dimensional approaches. IEEE Trans Syst Man Cybern Syst 43(4):996–1002

    Article  Google Scholar 

  4. Dachille F, Kreeger K, Chen B, Bitter I, Kaufman A (1998) High-quality volume rendering using texture mapping hardware. SIGGRAPH/Eurographics Workshop on Graphics Hardware’98, pp 69–76

  5. Fang L, Wang Y, Qiu B, Qian Y (2002) Fast maximum intensity projection algorithm using shear warp factorization and reduced resampling. Magn Reson Med 47:696–700

    Article  Google Scholar 

  6. Kiefer G, Lehmann H, Weese J (2006) Fast maximum intensity projections of large medical data sets by exploiting hierarchical memory architectures. IEEE Trans Inf Technol Biomed 10(2):385–394

    Article  Google Scholar 

  7. Kye H (2009) Efficient maximum intensity projection using SIMD instruction and streaming memory transfer. J Korea Multimedia Soc 12(4):512–520

    Google Scholar 

  8. Lacroute P (1995) Fast volume rendering using a shear-warp factorization of the viewing transformation. Technical Report: CSL-TR-95-678

  9. Lacroute P, Levoy M (1994) Fast volume rendering using a shear-warp factorization of the viewing transformation. Proceeding SIGGRAPH ‘94 Proceedings of the 21st annual conference on Computer graphics and interactive techniques, pp 451–458

  10. Liu Y, Zhang X, Cui J (2010) Visual analysis of child-adult interactive behaviors in video sequences. 16th International Conference on Virtual Systems and Multimedia, pp 26–33

  11. Liu Y, Cui J, Zhao H, Zha H (2012) Fusion of low-and high-dimensional approaches by trackers sampling for generic human motion tracking. 21st International Conference on Pattern Recognition, pp 898–901

  12. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2Activity: recognizing complex activities from sensor data. Proceedings of the 24th International Conference on Artificial Intelligence, pp 1617-1623

  13. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181(12):108–115

    Article  Google Scholar 

  14. Liu L, Cheng L, Liu Y, Jia Y, Rosenblum DS (2016) Recognizing complex activities by a probabilistic interval-based model. Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pp 1266–1272

  15. Lu Y, Wei Y, Liu L, Zhong J, Sun L, Liu Y (2017) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimedia Tools Appl 76(8):10701–10719

    Article  Google Scholar 

  16. McFarlin DS, Arbatov V, Franchetti F, Püschel M (2011) Automatic SIMD vectorization of fast fourier transforms for the larrabee and AVX instruction sets. Proceedings of the international conference on Supercomputing, pp 265–274, Tucson, Arizona, USA

  17. Mensmann J, Ropinski T, Hinrichs KH (2010) An advanced volume raycasting technique using GPU stream processing. International Conference on Computer Graphics Theory and Applications, pp 190–198

  18. Mora B, Ebert DS (2005) Low-complexity maximum intensity projection. ACM Trans Graph 24(4):1392–1416

    Article  Google Scholar 

  19. Mroz L, König A, Gröller E (1999) Real-time maximum intensity projection. Data Visualization ‘99, pp 135–144

  20. Mroz L, Hauser H, Gröller E (2000) Interactive high-quality maximum intensity projection. Comput Graphics Forum 19(3):341–350

    Article  Google Scholar 

  21. Pekar V, Hempel D, Kiefer G, Busch M, Weese J (2003) Efficient visualization of large medical image datasets on standard PC hardware. Joint EUROGRAPHICS - IEEE TCVG Symposium on Visualization, pp 135–140

  22. Rezk-Salama C, Engel K, Bauer M, Greiner G, Ertl T (2000) Interactive volume rendering on standard PC graphics hardware using multi-textures and multi-stage-rasterization. Proceedings of SIGGRAPH/Eurographics Workshop on Graphics Hardware’00, pp 109–118

  23. Sabella P (1988) A rendering algorithm for visualizing 3D scalar fields. ACM SIGGRAPH 1988 Proceedings of the 15th annual conference on Computer graphics and interactive techniques, pp 51–58

  24. Schreiner S, Galloway RL Jr (1993) A fast maximum-intensity projection algorithm for generating magnetic resonance angiograms. IEEE Trans Med Imaging 12(1):50–57

    Article  Google Scholar 

  25. Vollrath JE, Weiskopf D, Ertl T (2005) A generic software framework for the gpu volume rendering pipeline. Proceedings of Vision, Modeling, and Visualization, pp 391–398

  26. Zekri AS (2014) Enhancing the matrix transpose operation using intel avx instruction set extension. Int J Comput Sci Inf Technol (IJCSIT) 6(3):67–78

    Google Scholar 

  27. Zhao K, Sakamoto N, Koyamada K (2014) Fused visualization for large-scale time-varying volume data with adaptive particle-based rendering. AsiaSim 2014, 14th International Conference on Systems Simulation, pp 228–242

Download references

Acknowledgements

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (No. 2017R1A2B3011475).

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Authors and Affiliations

  1. Division of Computer Engineering, Hansung University, 116 Samseongyoro-16Gil Seongbuk-gu, Seoul, 136-792, South Korea

    Heewon Kye

  2. Department of Information Systems Engineering, Hansung University, 116 Samseongyoro-16Gil Seongbuk-gu, Seoul, 136-792, South Korea

    Se Hee Lee

  3. School of Computer Science & Engineering, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul, 156-743, South Korea

    Jeongjin Lee

Authors
  1. Heewon Kye
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  2. Se Hee Lee
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  3. Jeongjin Lee
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Corresponding author

Correspondence to Jeongjin Lee.

Appendix

Appendix

1.1 16 × 16 matrix transposition. AVX2 instructions and the processing examples

Table 6 16 × 16 tile transposition using AVX2
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Fig. 12
figure 12figure 12figure 12

The process of 16 × 16 matrix transposition. a Original, b after phase 1, c after phase 2, d after phase 3, and e after phase 4

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Kye, H., Lee, S.H. & Lee, J. CPU-based real-time maximim intensity projection via fast matrix transposition using parallelization operations with AVX instruction set. Multimed Tools Appl 77, 15971–15994 (2018). https://doi.org/10.1007/s11042-017-5171-2

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