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Journal of Visualization

, Volume 21, Issue 4, pp 637–647 | Cite as

A novel robust color gradient estimator for photographic volume visualization

  • Bin Zhang
  • Zhiguang Zhou
  • Yubo Tao
  • Hai Lin
Regular Paper
  • 54 Downloads

Abstract

Photographic volume visualization has been widely applied in various fields ranging from medicine to biology. Different from scalar volume data, photographic volume data are directly captured by means of the modern cryo-imaging systems. The voxels are recorded as RGB vectors, which makes it difficult to estimate accurate gradient for the shading and the design of transfer functions. In this paper, we propose a robust color gradient estimation method to produce accurate and robust gradient results for photographic volumes. First, a robust color morphological gradient (RCMG) operator is employed to estimate the gradient in a dominant direction and the low-pass filters are then applied to reduce the effects of noises. Then, an aggregation operator is applied to estimate the accurate gradient directions and the gradient magnitudes. Based on the obtained color gradients, the shading effects of internal materials are enhanced and the features can be better specified in a 2D transfer function space. At last, the effectiveness of the robust gradient estimation for photographic volume is demonstrated based on a large number of experimental rendering results, especially for those noisy photographic volume data sets.

Graphical abstract

Keywords

Photographic volume Color gradient Volume rendering Transfer function 

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers for their valuable comments. This work was supported by NSF of China Project Nos. 61303133, 61472354, the National Statistical Scientific Research Project No. 2015LD03, the China Postdoctoral Science Foundation No. 2015M571846, the Zhejiang Science and Technology Plan of China No. 2014C31057, and the National Key Technology Research and Development Program of the Ministry of Science and Technology of China under Grant 2014BAK14B01.

References

  1. Correa C, Hero R, Ma KL (2011) A comparison of gradient estimation methods for volume rendering on unstructured meshes. IEEE Trans Vis Comput Gr 17(3):305–319.  https://doi.org/10.1109/TVCG.2009.105 CrossRefGoogle Scholar
  2. Ebert DS, Morris CJ, Rheingans P, Yoo TS (2002) Designing effective transfer functions for volume rendering from photographic volumes. IEEE Trans Vis Comput Gr 8(2):183–197CrossRefGoogle Scholar
  3. Ercan G, Whyte P (2001) Digital image processing. US Patent 6,240,217Google Scholar
  4. Evans AN, Liu XU (2006) A morphological gradient approach to color edge detection. IEEE Trans Image Process 15(6):1454–1463CrossRefGoogle Scholar
  5. Gargesha M, Qutaish M, Roy D, Steyer G, Bartsch H, Wilson DL (2009) Enhanced volume rendering techniques for high-resolution color cryo-imaging data. In: SPIE Medical Imaging, International Society for Optics and Photonics, p 72622V.  https://doi.org/10.1117/12.813756
  6. Kindlmann G, Durkin JW (1998) Semi-automatic generation of transfer functions for direct volume rendering. In: Proceedings of the 1998 IEEE symposium on volume visualization, ACM, pp 79–86Google Scholar
  7. Kniss J, Premoze S, Hansen C, Shirley P, McPherson A (2003) A model for volume lighting and modeling. IEEE Trans Vis Comput Gr 9(2):150–162CrossRefGoogle Scholar
  8. Lee B, Kwon K, Shin BS (2016) Interactive high-quality visualization of color volume datasets using GPU-based refinements of segmentation data. J X Ray Sci Technol 24(4):537–548.  https://doi.org/10.3233/XST-160572 CrossRefGoogle Scholar
  9. Levoy M (1988) Display of surfaces from volume data. IEEE Comput Gr Appl 8(3):29–37CrossRefGoogle Scholar
  10. Max N (1995) Optical models for direct volume rendering. IEEE Trans Vis Comput Gr 1(2):99–108CrossRefGoogle Scholar
  11. Mittal A, Sofat S, Hancock E (2012) Detection of edges in color images: a review and evaluative comparison of state-of-the-art techniques. In: Proceedings of the third international conference on autonomous and intelligent systems, AIS’12, pp 250–259.  https://doi.org/10.1007/978-3-642-31368-4_30
  12. Morris CJ, Ebert D (2002) Direct volume rendering of photographic volumes using multi-dimensional color-based transfer functions. In: Proceedings of the symposium on data visualisation 2002, Eurographics Association, pp 115-ffGoogle Scholar
  13. Nezhadarya E, Ward RK (2011) A new scheme for robust gradient vector estimation in color images. IEEE Trans Image Process 20(8):2211–2220MathSciNetCrossRefMATHGoogle Scholar
  14. Pfister H, Lorensen B, Bajaj C, Kindlmann G, Schroeder W, Avila LS, Raghu K, Machiraju R, Lee J (2001) The transfer function bake-off. IEEE Comput Gr Appl 21(3):16–22CrossRefGoogle Scholar
  15. Plataniotis KN, Venetsanopoulos AN (2000) Color image processing and applications. Springer, BerlinCrossRefGoogle Scholar
  16. Roettger S, Bauer M, Stamminger M (2005) Spatialized transfer functions. In: Proceedings of the seventh joint eurographics/IEEE VGTC conference on visualization, Eurographics Association, pp 271–278Google Scholar
  17. Roy D, Steyer GJ, Gargesha M, Stone ME, Wilson DL (2009) 3D cryo-imaging: a very high-resolution view of the whole mouse. Anat Rec 292(3):342–351CrossRefGoogle Scholar
  18. Russo F, Lazzari A (2005) Color edge detection in presence of gaussian noise using nonlinear prefiltering. IEEE Trans Instrum Meas 54(1):352–358CrossRefGoogle Scholar
  19. Sereda P, Bartroli AV, Serlie IW, Gerritsen FA (2006) Visualization of boundaries in volumetric data sets using LH histograms. IEEE Trans Vis Comput Gr 12(2):208–218CrossRefGoogle Scholar
  20. Spitzer V, Ackerman MJ, Scherzinger AL, Whitlock D (1996) The visible human male: a technical report. J Am Med Inform Assoc 3(2):118–130CrossRefGoogle Scholar
  21. Vandenberghe ME, Hrard AS, Souedet N, Sadouni E, Santin MD, Briet D, Carr D, Schulz J, Hantraye P, Chabrier PE, Rooney T, Debeir T, Blanchard V, Pradier L, Dhenain M, Delzescaux T (2016) High-throughput 3D whole-brain quantitative histopathology in rodents. Sci Rep 6:20958.  https://doi.org/10.1038/srep20958 CrossRefGoogle Scholar
  22. Zhang B, Tao Y, Lin H, Dong F, Clapworthy G (2015) Intuitive transfer function design for photographic volumes. J Vis 18(4):571–580.  https://doi.org/10.1007/s12650-014-0267-5 CrossRefGoogle Scholar

Copyright information

© The Visualization Society of Japan 2018

Authors and Affiliations

  1. 1.State Key Lab of CAD&CGZhejiang UniversityHangzhouChina
  2. 2.School of InformationZhejiang University of Finance and EconomicsHangzhouChina

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