Abstract
A novel approach to the development of direct volume rendering algorithms is proposed. The approach is based on the assessment of the visualization quality. Analysis of rendering artifacts and various assessment methods is done using 3D reconstructed medical tomograms as test datasets. Analysis of the previous research on methods of 3D visualization quality assessment and quality improvement is presented. 3D visualization artifacts and quality measurement method (quantitative estimation) is proposed. The method does not require any ground truth image; hence, it is a universal approach to measure the quality of the 3D-reconstruction by any ray casting technique. The method is based on generation of the reference image as a mathematical expectation for a set of 3D visualizations obtained via the jittering technique. Start position of the rays are being shifted randomly towards their directions. To estimate the deviation of a pixel value from mathematical expectation, we use PSNR metrics, which is traditional metrics in signal and image processing and measures deviation in the decibel scale. The conditions of a proper use of the technique are discussed. A new virtual samplings method with preintegration is proposed to reduce sampling artifacts in the ray casting algorithm. The novelty of the method consists in using pre-integrated classification instead of post-classification in the virtual sampling method. A novel approach to 3D visualization algorithms development based on analysis of a ray casting method in quality-performance space is demonstrated by comparing the state-of-the-art ray casting methods and the proposed method. The comparative analysis revealed an advantage of the classical pre-integrated method in the case of using trilinear filtering without local shading. It also showed the advantage of the proposed virtual sampling method with pre-integration in the case of using tricubic filtering with local lighting.
Similar content being viewed by others
References
Gavrilov, N.I. and Turlapov, V.E., Approach to optimization of GPU-algorithm for the volume ray casting method to level of using in a virtual dissecting table (in Russian), Sci. Visualization, 2012, vol. 4, no. 2, pp. 21–56 (http://sv-journal.com/2012-2/).
Lee, B., Yun, J., Seo, J., Shim, B., Shin, Y.G., and Kim, B., Fast high-quality volume ray casting with virtual samplings, IEEE Trans. Visualization Comput. Graphics, 2010, vol. 16, no. 6, pp. 1525–1532.
Wunderlich, A. and Noo, F., Evaluation of image noise in fan-beam x-ray computed tomography, Proc. of the IEEE Eng. Med. Biol. Soc. Conf., 2008, 2713-6 (http://www.ncbi.nlm.nih.gov/pubmed/19163265).
Pan, N., Liu, H., de Ruiter, N., and Grasset, R., Improving the image quality of spectral CT volume rendering, Proc. of the 24th Int. Conf. “Image and Vision Computing New Zealand” (IVCNZ’ 09), 2009. pp. 203–208.
Storozhilova, M., Lukin, A., Yurin, D., and Sinitsyn, V., 2.5D Extension of neighborhood filters for noise reduction in 3D medical CT images, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, vol. 7870, pp. 1–16.
Nelson, M., Optical models for direct volume rendering, IEEE Trans. Visualization Comput. Graphics, 1995, vol. 1, no. 2, pp. 99–108.
Engel, K., Hadwiger, M., Kniss, J., and RezkSalama, C., Real-Time Volume Graphics (Tutorial), Eurographics Association, 2006.
Sigg, C. and Hadwiger, M., Fast third-order texture filtering, in GPU Gems 2, Matt Pharr, Ed., Addison-Wesley, 2005, pp. 313–329.
Guthe, S. and Strasser, W., Advanced techniques for high-quality multi-resolution volume rendering, Comput. Graphics, 2004, vol. 28, no. 1, pp. 51–58.
Engel, K., Kraus, M., and Ertl, T., High-quality preintegrated volume rendering using hard-ware-accelerated pixel shading, Proc. of Graphics Hardware 2001, pp. 9–16.
Scharsach, H., Advanced GPU ray-casting, Proc. of CESCG 5, 2005, pp. 67–76.
El Hajjar, J.F., et al., Second order pre-integrated volume rendering, Visualization Symposium, 2008, PacificVIS’08, IEEE Pacific. IEEE, 2008, pp. 9–16.
Knoll, A., Hijazi, Y., Westerteiger, R., Schott, M., Hansen, C., and Hagen, H., Volume ray casting with peak finding and differential sampling, IEEE Trans. Visualization Comput. Graphics, 2009, vol. 15, no. 6, pp. 1571–1578.
Bogolepov, D., Bugaev, I., Belokamenskaya, A., and Turlapov, V., Anti-aliasing in the implementation of pre-integrated rendering for visualization of three-dimensional scalar fields on the GPU, Sci. Visualization, 2012, vol. 4, no. 4, pp. 2–16 (http://sv-journal.com/2012-4/).
Balsa Rodriguez, M., Gobbetti, E., Iglesias Guitian, J.A., Makhinya, M., Marton, F., and Pajarola, R., A survey of compressed GPU-based direct volume rendering, Eurographics 2013 papers, pp. 117–136.
Kajiya, J.T., The rendering equation, Proc. ACM SIG-GRAPH, 1986, vol. 20, no. 4, pp. 143–150.
Author information
Authors and Affiliations
Corresponding author
Additional information
Original Russian Text © N.I. Gavrilov, V.E. Turlapov, 2014, published in Programmirovanie, 2014, Vol. 40, No. 4.
Rights and permissions
About this article
Cite this article
Gavrilov, N.I., Turlapov, V.E. Novel approach to development of direct volume rendering algorithms based on visualization quality assessment. Program Comput Soft 40, 174–184 (2014). https://doi.org/10.1134/S0361768814040045
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1134/S0361768814040045