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.
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References
Bajaj CL, Pascucci V, Schikore DR (1997) The contour spectrum. Visualization 1997, Proceedings, pp 167–173, Oct 1997
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
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
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
Correa C, Ma K-L (2008) Size-based transfer functions: a new volume exploration technique. IEEE Trans Vis Comput Graph 14(6): 1380–1387
Correa C, Ma K-L (2009) The occlusion spectrum for volume classification and visualization. IEEE Trans Vis Comput Graph 15(6): 1465–1472
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
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
Kindlmann G, Durkin JW (1998) Semi-automatic generation of transfer functions for direct volume rendering. IEEE Symp Vol Vis pp 79–86, Oct 1998
Kniss J, Kindlmann G, Hansen C (2002) Multidimensional transfer functions for interactive volume rendering. IEEE Trans Vis Comput Graph 8(3): 270–285
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
OsiriX. http://www.osiriximaging.com/resources/, 2010. last visited: 2010/04/22
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
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7): 629–639
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
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
Preim B, Bartz D (2007) Visualization in medicine. Theory, algorithms, and applications 1st edn. Morgan Kaufmann Series in Computer Graphics, Morgan Kaufmann
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
Roettger S, Bauer M, Stamminger M (2005) Spatialized transfer functions. In: Proceedings of EUROVIS 2005: Eurographics/IEEE VGTC symposium on visualization, pp 271–278
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
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
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
VolVis. http://www.volvis.org, 2009. last visited: 2009/12/11
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
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
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
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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
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DOI: https://doi.org/10.1007/s11548-010-0480-1