Skip to main content
SpringerLink
Log in

2D Histogram based volume visualization: combining intensity and size of anatomical structures

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Surgical planning requires 3D volume visualizations based on transfer functions (TF) that assign optical properties to volumetric image data. Two-dimensional TFs and 2D histograms may be employed to improve overall performance.

Methods

Anatomical structures were used for 2D TF definition in an algorithm that computes a new structure-size image from the original data set. The original image and structure-size data sets were used to generate a structure-size enhanced (SSE) histogram. Alternatively, the gradient magnitude could be used as second property for 2D TF definition. Both types of 2D TFs were generated and compared using subjective evaluation of anatomic feature conspicuity.

Results

Experiments with several medical image data sets provided SSE histograms that were judged subjectively to be more intuitive and better discriminated different anatomical structures than gradient magnitude-based 2D histograms.

Conclusions

In clinical applications, where the size of anatomical structures is more meaningful than gradient magnitude, the 2D TF can be effective for highlighting anatomical structures in 3D visualizations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Bajaj CL, Pascucci V, Schikore DR (1997) The contour spectrum. Visualization 1997, Proceedings, pp 167–173, Oct 1997

  2. Birchfield ST, Rangarajan S (2005) Spatiograms versus histograms for region-based tracking. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 2. pp 1158–1163, June 2005

  3. Botha CP, Post FH (2002) New technique for transfer function specification in direct volume rendering using real-time visual feedback. In: Seong K, Mun (eds) Medical imaging 2002: visualization, image-guided procedures, and display, vol 4681. pp 349–356. SPIE

  4. Chan M-Y, Wu Y, Mak W-H, Chen W, Qu H (2009) Perception-based transparency optimization for direct volume rendering. IEEE Trans Vis Comput Graph 15(6): 1283–1290

    Article  PubMed  Google Scholar 

  5. Correa C, Ma K-L (2008) Size-based transfer functions: a new volume exploration technique. IEEE Trans Vis Comput Graph 14(6): 1380–1387

    Article  PubMed  Google Scholar 

  6. Correa C, Ma K-L (2009) The occlusion spectrum for volume classification and visualization. IEEE Trans Vis Comput Graph 15(6): 1465–1472

    Article  PubMed  Google Scholar 

  7. Engel K, Kraus M, Ertl T (2001) High-quality pre-integrated volume rendering using hardware-accelerated pixel shading. In: HWWS 2001: Proceedings of the ACM SIGGRAPH/EUROGRAPHICS workshop on graphics hardware. ACM, New York, pp 9–16

  8. Hadwiger M, Laura F, Rezk-Salama C, Hllt T, Geier G, Pabel T (2008) Interactive volume exploration for feature detection and quantification in industrial CT data. IEEE Trans Vis Comput Graph 14(6): 1507–1514

    Article  PubMed  Google Scholar 

  9. Kindlmann G, Durkin JW (1998) Semi-automatic generation of transfer functions for direct volume rendering. IEEE Symp Vol Vis pp 79–86, Oct 1998

  10. Kniss J, Kindlmann G, Hansen C (2002) Multidimensional transfer functions for interactive volume rendering. IEEE Trans Vis Comput Graph 8(3): 270–285

    Article  Google Scholar 

  11. Maciejewski R, Woo I, Chen W, Ebert D (2009) Structuring feature space: a non-parametric method for volumetric transfer function generation. IEEE Trans Vis Comput Graph 15(6): 1473–1480

    Article  PubMed  Google Scholar 

  12. OsiriX. http://www.osiriximaging.com/resources/, 2010. last visited: 2010/04/22

  13. Pekar V, Wiemker R, Hempel D (2001) Fast detection of meaningful isosurfaces for volume data visualization. In: VIS 2001: Proceedings of the conference on visualization 2001. IEEE Computer Society, Washington, pp 223–230

  14. Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7): 629–639

    Article  Google Scholar 

  15. Pfister H, Lorensen B, Bajaj C, Kindlmann G, Schroeder W, Avila LS, Raghu KM, Machiraju R, Lee J (2001) The transfer function bake-off. IEEE Comput Graph Appl 21(3): 16–22

    Article  Google Scholar 

  16. Praßni J-S, Ropinski T, Hinrichs KH (2009) Efficient boundary detection and transfer function generation in direct volume rendering. In: Proceedings of the vision, modeling, and visualization workshop 2009 (VMV09), pp 285–294. Otto-von-Guericke- Universität Magdeburg

  17. Preim B, Bartz D (2007) Visualization in medicine. Theory, algorithms, and applications 1st edn. Morgan Kaufmann Series in Computer Graphics, Morgan Kaufmann

    Google Scholar 

  18. Reitinger B, Zach C, Bornik A, Beichel R (2004) User- centric transfer function specification in augmented reality. In: Proceedings of WSCG (Plzen, Czech Republic, February 2004), pp 355–362

  19. Roettger S, Bauer M, Stamminger M (2005) Spatialized transfer functions. In: Proceedings of EUROVIS 2005: Eurographics/IEEE VGTC symposium on visualization, pp 271–278

  20. Selver MA, Güzeliş C (2009) Semiautomatic transfer function initialization for abdominal visualization using self-generating hierarchical radial basis function networks. IEEE Trans Vis Comput Graph 15(3): 395–409

    Article  PubMed  Google Scholar 

  21. Sereda P, Bartroli AV, Serlie IWO, Gerritsen FA (2006) Visualization of boundaries in volumetric data sets using LH histograms. IEEE Trans Vis Comput Graph 12(2): 208–218

    Article  PubMed  Google Scholar 

  22. Tappenbeck A, Preim B, Dicken V (2006) Distance-based transfer function design: Specification methods and applications. In: Simulation und visualisierung, pp 259–274. SCS-Verlag

  23. VolVis. http://www.volvis.org, 2009. last visited: 2009/12/11

  24. Weber GH, Scheuermann G (2002) Topology-based transfer function design. In: Villanueva JJ (ed) Proceedings of the second IASTED international conference on visualization, imaging, and image processing, pp 527–532. ACTA Press

  25. Wesarg S, Kirschner M (2009) Gradient magnitude vs. feature size: comparing 2D histograms for transfer function specification. In: CGI 2009: Computer graphics international. ACM, New York, pp 115–119

  26. Zhou J, Takatsuka M (2009) Automatic transfer function generation using contour tree controlled residue flow model and color harmonics. IEEE Trans Vis Comput Graph 15(6): 1481–1488

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Interactive Graphics Systems Group (GRIS), TU Darmstadt, Fraunhoferstr. 5, 64283, Darmstadt, Germany

    S. Wesarg & M. Kirschner

  2. Department of Diagnostic and Interventional Radiology, Goethe University, Theodor-Stern-Kai 7, 60590, Frankfurt am Main, Germany

    M. F. Khan

Authors
  1. S. Wesarg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Kirschner
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. M. F. Khan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Wesarg.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wesarg, S., Kirschner, M. & Khan, M.F. 2D Histogram based volume visualization: combining intensity and size of anatomical structures. Int J CARS 5, 655–666 (2010). https://doi.org/10.1007/s11548-010-0480-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-010-0480-1

Keywords

Search

Navigation