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Fast Medical Volume Decompression Using GPGPU

GPGPU를 이용한 고속 의료 볼륨 영상의 압축 복원

  • 계희원 (한성대학교 정보시스템공학과)
  • Received : 2011.10.20
  • Accepted : 2012.03.16
  • Published : 2012.05.31

Abstract

For many medical imaging systems, volume datasets are stored as a compressed form, so that the dataset has to be decompressed before it is visualized. Since the decompression process takes quite a long time, we present an acceleration method for medical volume decompression using GPU. Our method supports that both lossy and lossless compression and progressive refinement is possible to satisfy variable user requirements. Moreover, our decompression method is well parallelized for GPU so that the decompression takes a very short time. Finally, we designed that the decompression and volume rendering work in one framework so that the selective decompression is available. As a result, we gained additional improvement in volume decompression.

많은 의료영상 시스템에서 의료 볼륨 데이터는 압축된 형태로 저장되어 있으며, 압축된 데이터는 가시화 이전에 압축 복원을 수행해야 한다. 압축 복원은 상당한 시간이 소모되기 때문에 본 연구는 삼차원 의료영상의 고속 복원 방식을 제안한다. 제안 방법은 의료영상의 특수성에 대한 사용자 요구를 감안하여, 손실과 무손실 압축을 모두 제공하며 점진적 개선(progressive refinement) 복원 속성을 갖는다. 그리고 그래픽스처리장치(GPU)를 이용한 병렬화를 수행하여 매우 짧은 시간 내에 압축 복원이 수행된다. 마지막으로 압축 복원과 볼륨 가시화를 연계하여 선택적 압축 복원 방법이 가능하며, 이를 통하여 볼륨 압축 복원의 추가적 성능 향상을 얻었다.

Keywords

References

  1. M. Levoy, "Display of Surfaces from Volume Data," IEEE Computer Graphics and Application, Vol.8, No.3, pp. 29-37, 1988. https://doi.org/10.1109/38.511
  2. L. Bottou, P. Haffner, P. Howard, P. Simard, Y. Bengio, and Y. LeCun, "High quality document image compression using DjVu," Journal of Electronic Imaging, Vol.7, No.3, pp. 410-425, 1998. https://doi.org/10.1117/1.482609
  3. Z. Xiong, X. Wu, S. Cheng, and J. Hua, "Lossyto- Lossless Compression of Medical Volumetric Data using Three-Dimensional Integer Wavelet Transforms," IEEE Transactions on Medical Imaging, Vol.22, No.3, pp. 459-470, 2003. https://doi.org/10.1109/TMI.2003.809585
  4. N. Fout and K.L. Ma, "Transform Coding for Hardware-accelerated Volume Rendering," IEEE Transaction on Visualization and Computer Graphics, Vol.13, No.6, pp. 1600-1607, 2007. https://doi.org/10.1109/TVCG.2007.70516
  5. E.B. Lum, K.L. Ma, and J. Clyne, "Texture Hardware Assisted Rendering of Time-Varying Volume Data," Proc. of the Conference on Visualization '01, pp. 263-270, 2001.
  6. M. Kraus and T. Ertl, "Adaptive Texture Maps," Proc. of the ACM SIGGRAPH / EUROGRAPHICS Conference on Graphics Hardware, pp. 7-15, 2002.
  7. M. Pratt, C. Chu, and S. Wong, "Volume Compression of MRI Data using Zerotrees of Wavelet Coefficients," Proc. SPIE Wavelet Applications in Signal and Image Processing IV, Vol.2825, pp. 752-763, 1996.
  8. A. Said and W. Pearlman, "A New, Fast and Efficient Image Codec Based on Set Partitioning," IEEE Trans. Circuits Syst. Video Technol., Vol.6, No.3, pp. 243-250, 1996. https://doi.org/10.1109/76.499834
  9. D. Taubman, "High Performance Scalable Image Compression with EBCOT," IEEE Trans. Image Processing, Vol.9, No.7, pp. 1158-1170, 2000. https://doi.org/10.1109/83.847830
  10. K. Engel, M. Hadwiger, J.M. Kniss, C. Rezk- Salama, and D. Weiskopf, Real-Time Volume Graphics, Wellesley, Massachusetts, 2006.
  11. J.M. Shapiro, "Embedded Image Coding using Zerotrees of wavelet Coefficients," IEEE Transactions on Signal Processing, Vol.41, No.12, pp. 3445-3462, 1993. https://doi.org/10.1109/78.258085
  12. CUDA, Available online (http://www.nvidia.com/object/cuda_home_new.html), 2012
  13. 이만희, 박인규, 원석진, 조성대, "GPU를 이용한 DWT 및 JPEG2000의 고속 연산," 전자공학회논문지 제44권, 제6호, pp. 9-15, 2007.
  14. DirectX, Available online (http://msdn.microsoft.com/en-us/directx), 2012
  15. 계희원, 김준호, "GPGPU 환경에서 최대휘소투영 렌더링의 고속화 방법," 멀티미디어학회논문지, 14권 8호, pp. 981-991, 2011.
  16. M. Levoy, "Efficient Ray Tracing of Volume Data," ACM Transactions on Graphics, Vol.9, No.3, pp.245-261, 1990. https://doi.org/10.1145/78964.78965
  17. W. Li, K. Mueller, and A. Kaufman, "Empty Space Skipping and Occlusion Clipping for Texture-Based Volume Rendering," Proc. of IEEE Visualization Conference, pp. 317-324, 2003.
  18. A. Balevic, "Parallel Variable-Length Encoding on GPGPUs," Proc. of the 2009 International Conference on Parallel Processing, Springer-Verlag, Berlin, Heidelberg, pp. 26-35, 2009.
  19. M.A. O'Neil and M. Burtscher, "Floating-point data compression at 75 Gb/s on a GPU," Proc. of the Fourth Workshop on General Purpose Processing on Graphics Processing Units, pp. 7, 2011.
  20. P. Lindstrom and J.D. Cohen, "On-the-Fly Decompression and Rendering of Multiresolution Terrain," Proc. of IEEE Visualization 2009, Atlantic City, NJ , USA, 2009.
  21. T. Acharya and P.S. Tsai, JPEG2000 Standard for Image Compression: Concepts, Algorithms and VLSI Architectures, John Wiley & Sons, New Jersey, 2005.

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