Principal Component Analysis Techniques for Visualization of Volumetric Data

  • Salaheddin Alakkari
  • John DinglianaEmail author


We investigate the use of Principal Component Analysis (PCA) for the visualization of 3D volumetric data. For static volume datasets, we assume, as input training samples, a set of images rendered from spherically distributed viewing positions, using a state-of-the-art volume rendering technique. We compute a high-dimensional eigenspace, that we can then use to synthesize arbitrary views of the dataset with minimal computation at run-time. Visual quality is improved by subdividing the training samples using two techniques: cell-based decomposition into equally sized spatial partitions and a more generalized variant, which we referred to as band-based PCA. The latter approach is further extended for the compression of time-varying volume data directly. This is achieved by taking, as input, full 3D volumes comprised by the time-steps of the time-varying sequence and generating an eigenspace of volumes. Results indicate that, in both cases, PCA can be used for effective compression with minimal loss of perceptual quality, and could benefit applications such as client-server visualization systems.



This research has been conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number 13/IA/1895. The VisMale Head dataset is courtesy of the Visible Human Project at the U.S. National Library of Medicine. The Tooth was obtained from the Volume Library of Stefan Roettger. The Chest was obtained from the DICOM sample datasets provided by OsiriX. The Supernova data set is made available by Dr. John Blondin at the North Carolina State University through US Department of Energy’s SciDAC Institute for Ultrascale Visuaization. The Turbulent Vortex dataset obtained from Time Varying Volume Data Reporsitory at UC Davis.


  1. 1.
    Bethel, W.: Visualization dot com. IEEE Comput. Graph. Appl. 20(3), 17–20 (2000)CrossRefGoogle Scholar
  2. 2.
    Broersen, A., van Liere, R., Heeren, R.M.: Comparing three pca-based methods for the visaulization of imaging spectroscopy data. In: Proceedings of the Fifth IASTED International Conference on Visualization, Imaging and Image Processing, pp. 540–545 (2005)Google Scholar
  3. 3.
    Callahan, S.P., Bavoil, L., Pascucci, V., Silva, C.T.: Progressive volume rendering of large unstructured grids. IEEE Trans. Vis. Comput. Graph. 12(5), 1307–1314 (2006)CrossRefGoogle Scholar
  4. 4.
    Chen, B., Kaufman, A., Tang, Q.: Image-based rendering of surfaces from volume data. In: Mueller, K., Kaufman, A.E. (eds) Volume Graphics 2001: Proceedings of the Joint IEEE TCVG and Eurographics Workshop in Stony Brook, pp. 279–295, New York, USA, 21–22 June 2001, Springer Vienna, Vienna (2001)Google Scholar
  5. 5.
    Choi, J.-J., Shin, Y.G.: Efficient image-based rendering of volume data. In: Computer Graphics and Applications, 1998. Pacific Graphics ’98. Sixth Pacific Conference on, pp. 70–78, 226 (1998)Google Scholar
  6. 6.
    Engel, K., Ertl, T.: Texture-based volume visualization for multiple users on the world wide web. In: Virtual Environments, pp. 115–124. Springer, Berlin (1999)Google Scholar
  7. 7.
    Fout, N., Ma, K.L.: Transform coding for hardware-accelerated volume rendering. IEEE Trans. Vis. Comput. Graph. 13(6), 1600–1607 (2007)CrossRefGoogle Scholar
  8. 8.
    Frank, S., Kaufman, A. 2005: Distributed volume rendering on a visualization cluster. In: Ninth International Conference on Computer Aided Design and Computer Graphics (CAD-CG’05)Google Scholar
  9. 9.
    Gong, S., McKenna, S., Collins, J.J.: An investigation into face pose distributions. In: Proceedings of the Second International Conference on Automatic Face and Gesture Recognition, pp. 265–270. IEEE, Hoboken (1996)Google Scholar
  10. 10.
    Gourier, N., Hall, D., Crowley, J.L.: Estimating face orientation from robust detection of salient facial features. In: ICPR International Workshop on Visual Observation of Deictic Gestures, Citeseer (2004)Google Scholar
  11. 11.
    Grabner, M., Bischof, H., Zach, C., Ferko, A.: Multiple eigenspaces for hardware accelerated image based rendering. In: Proceedings of ÖAGM, pp. 111–118.
  12. 12.
    Hadwiger, M., Kniss, J.M., Rezk-salama, C., Weiskopf, D., Engel, K.: Real-time, vol. Graphics. A. K, Peters Ltd., Natick, MA, USA (2006)Google Scholar
  13. 13.
    Kirby, M., Sirovich, L.: Application of the karhunen-loeve procedure for the characterization of human faces. IEEE Trans. Pattern Anal. Mach. Intell. 12(1), 103–108 (1990)CrossRefGoogle Scholar
  14. 14.
    Knittel, G., Parys, R.: PCA-based seeding for improved vector quantization. In: Proceedings of the First International Conference on Computer Imaging Theory and Applications (VISIGRAPP 2009), pp. 96–99 (2009)Google Scholar
  15. 15.
    Kohlmann, P., Boskamp, T., Köhn, A., Rieder, C., Schenk, A., Link, F., Siems, U., Barann, M., Kuhnigk, J.-M., Demedts, D., Hahn, H.K.: Remote visualization techniques for medical imaging research and image-guided procedures. In: Linsen, L., Hamann, B., Hege, H.-C. (eds.) Visualization in Medicine and Life Sciences III: Towards Making an Impact, pp. 133–154. Springer International Publishing, Cham (2016)Google Scholar
  16. 16.
    Kroes, T., Post, F.H., Botha, C.P.: Exposure Render: an interactive photo-realistic volume rendering framework. PLoS ONE 8 (2013)Google Scholar
  17. 17.
    Leonardis, A., Bischof, H.: Multiple eigenspaces by mdl. In: Proceedings 15th International Conference on Pattern Recognition. ICPR-2000, vol. 1, pp. 233–237 (2000)Google Scholar
  18. 18.
    Liu, S., Wang, B., Thiagarajan, J.J., Bremer, P.T., Pascucci, V.: Multivariate volume visualization through dynamic projections. In: IEEE 4th Symposium on Large Data Analysis and Visualization (LDAV), pp. 35–42 (2014)Google Scholar
  19. 19.
    Meyer, M., Pfister, H., Hansen, C., Johnson, C., Meyer, M., Pfister, H., Hansen, C., Johnson, C.: Image-based volume rendering with opacity light fields, Technical report, University of Utah (2005)Google Scholar
  20. 20.
    Moser, M., Weiskopf, D.: Interactive volume rendering on mobile devices. In: Vision, Modeling, and Visualization VMV, vol. 8, pp. 217–226 (2008)Google Scholar
  21. 21.
    Nishino, K., Sato, Y., Ikeuchi, K.: Eigen-texture method: Appearance compression based on 3D model. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1. IEEE, Hoboken (1999)Google Scholar
  22. 22.
    Poliakov, A.V., , Albright, E., Corina, D., Ojemann, G., Martin, R., Brinkley, J.: Server-based approach to web visualization of integrated 3D medical image data. In: Proceedings of the AMIA Symposium, pp. 533–537 (2001)Google Scholar
  23. 23.
    Qi, X., Tyler, J.M.: A progressive transmission capable diagnostically lossless compression scheme for 3D medical image sets. Inf. Sci. 175(3), 217–243 (2005)CrossRefGoogle Scholar
  24. 24.
    Santhanam, A., Min, Y., Dou, T., Kupelian, P., Low, D.A.: A client-server framework for 3D remote visualization of radiotherapy treatment space. Front. Oncol. 3 (2013)Google Scholar
  25. 25.
    Schubert, N., Scholl, I.: Comparing GPU-based multi-volume ray casting techniques. Comput. Sci. Res. Dev. 26(1), 39–50 (2011)CrossRefGoogle Scholar
  26. 26.
    Takemoto, S., Nakao, M., Sato, T., Sugiura, T., Minato, K., Matsuda, T.: Interactive volume visualization of microscopic images using feature space reduction. BME 51, U–6–U–6. (2013)
  27. 27.
    Tikhonova, A., Correa, C.D., Ma, K.-L.: Explorable images for visualizing volume data. In: IEEE Pacific Visualization Symposium, pp. 177–184 (2010)Google Scholar
  28. 28.
    Turk, M., Pentland, A.: Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)CrossRefGoogle Scholar
  29. 29.
    Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)CrossRefGoogle Scholar
  30. 30.
    Yang, M.-H.: Kernel eigenfaces vs. kernel fisherfaces: Face recognition using kernel methods. In: fgr, vol. 2, p. 215 (2002)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Graphics Vision and Visualisation Group, School of Computer Science and StatisticsTrinity CollegeDublinIreland

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