Healthc Inform Res.  2019 Oct;25(4):297-304. 10.4258/hir.2019.25.4.297.

Real-Time Computed Tomography Volume Visualization with Ambient Occlusion of Hand-Drawn Transfer Function Using Local Vicinity Statistic

• Kim J
• Ha T
• Kye H

• KMID: 2462228
• DOI: http://doi.org/10.4258/hir.2019.25.4.297

Abstract

OBJECTIVES
In this paper, we present an efficient method to visualize computed tomography (CT) datasets using ambient occlusion, which is a global illumination technique that adds depth cues to the output image. We can change the transfer function (TF) for volume rendering and generate output images in real time.
METHODS
In preprocessing, the mean and standard deviation of each local vicinity are calculated. During rendering, the ambient light intensity is calculated. The calculation is accelerated on the assumption that the CT value of the local vicinity of each point follows the normal distribution. We approximate complex TF forms with a smaller number of connected line segments to achieve additional acceleration. Ambient occlusion is combined with the existing local illumination technique to produce images with depth in real time.
RESULTS
We tested the proposed method on various CT datasets using hand-drawn TFs. The proposed method enabled real-time rendering that was approximately 40 times faster than the previous method. As a result of comparing the output image quality with that of the conventional method, the average signal-to-noise ratio was approximately 40 dB, and the image quality did not significantly deteriorate.
CONCLUSIONS
When rendering CT images with various TFs, the proposed method generated depth-sensing images in real time.

Keyword

Data Visualization; Computer Systems; Imaging; Three-Dimensional; Mathematical Computing

MeSH Terms

Acceleration
Computer Systems
Cues
Dataset
Lighting
Mathematical Computing
Methods
Signal-To-Noise Ratio

Figure

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  Figure 1 Ray casting method. The rays originating from each pixel pass through the volume data. The accumulated pixel color is output as the final image.

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  Figure 2 Comparison of lighting effects: (A) local illumination and (B) ambient occlusion (a global illumination technique).

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  Figure 3 Overall flow of the proposed method.

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  Figure 4 Incremental algorithm to calculate the mean value.

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  Figure 5 Example of piecewise linear transfer function.

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  Figure 6 Transfer function (TF) simplification method: (A) inputting TF, (B) finding the longest line starting at p0 through as many consecutive windows as possible, and (C) creating the next starting point and repeating the process.

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  Figure 7 Results of various rendering methods for volume datasets (HEAD, ABDOMEN, LOWER, and LIVER): (A) previous local illumination, (B) proposed ambient occlusion (Section II-2), (C) weighted average of (A) and (B), and (D) accelerated method (Section II-3) of (C).

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  Figure 8 (A) Hand drawn TF and (B) comparison of image quality for the proposed method. Upper line is for the HEAD dataset, and lower line is for the ABDOMEN, LOWER, and LIVER datasets. As the window size increases, the line segment quantity, which translates into computational time, and image quality (SNR) decrease. When the window size is approximately 10, we obtain relatively high-quality images (high SNR) in a short time. CT: computed tomography, TF: transfer function, SNR: signal-to-noise ratio.

Reference

1. Levoy M. Display of surfaces from volume data. IEEE Comput Graph Appl. 1988; 8(3):29–37.

2. Engel K, Hadwiger M, Kniss JM, Rezk-Salama C, Weiskopf D. Real-time volume graphics. Wellesley (MA): AK Peters Ltd.;2006.

3. Kruger J, Westermann R. Acceleration techniques for GPU-based volume rendering. In : Proceedings of the 14th IEEE Visualization; 2003 Oct 22–24; Seattle, WA:

4. Levoy M. Efficient ray tracing of volume data. ACM Trans Graph. 1990; 9(3):245–261.

5. Cai LL, Nguyen NP, Chui CK, Ong SH. Rule-enhanced transfer function generation for medical volume visualization. Comput Graph Forum. 2015; 34(3):121–130.

6. Phong BT. Illumination for computer generated pictures. Commun ACM. 1975; 18(6):311–317.

7. Nelson B, Kirby RM, Haimes R. GPU-based volume visualization from high-order finite element fields. IEEE Trans Vis Comput Graph. 2014; 20(1):70–83.

8. Berger M, Li J, Levine JA. A Generative Model for Volume Rendering. IEEE Trans Vis Comput Graph. 2019; 25(4):1636–1650.

9. Kroes T, Schut D, Eisemann E. Smooth probabilistic ambient occlusion for volume rendering. In : Engel W, editor. GPU pro 360 guide to GPGPU. Boca Raton (FL): CRC Press;2019. p. 305–316.

10. Ropinski T, Meyer-Spradow J, Diepenbrock S, Mensmann J, Hinrichs K. Interactive volume rendering with dynamic ambient occlusion and color bleeding. Comput Graph Forum. 2008; 27(2):567–576.

11. Hernell F, Ljung P, Ynnerman A. Local ambient occlusion in direct volume rendering. IEEE Trans Vis Comput Graph. 2010; 16(4):548–559.

12. Diaz J, Yela Reneses H, Vazquez Alcocer PP. Vicinity occlusion maps: enhanced depth perception of volumetric models. In : Proceedings of the Computer Graphics International Conference; 2008 Jun 9–11; Istanbul, Turkey. p. 56–63.

13. Staib J, Grottel S, Gumhold S. Visualization of particle-based data with transparency and ambient occlusion. Comput Graph Forum. 2015; 34(3):151–160.

14. Nam J, Kye H. Fast ambient occlusion volume rendering using local statistics. J Korea Multimed Soci. 2015; 18(2):158–167.

15. Maciejewski R, Jang Y, Woo I, Janicke H, Gaither KP, Ebert DS. Abstracting attribute space for transfer function exploration and design. IEEE Trans Vis Comput Graph. 2013; 19(1):94–107.

16. Srivastava V, Chebrolu U, Mueller K. Interactive transfer function modification for volume rendering using compressed sample runs. In : Proceedings of the Computer Graphics International Conference; 2003 Jul 9–11; Tokyo, Japan. p. 8–13.

17. Pfister H, Lorensen B, Bajaj C, Kindlmann G, Schroeder W, Avila LS, et al. The transfer function bake-off. IEEE Comput Graph Appl. 2001; 21(3):16–22.

18. Ropinski T, Praßni JS, Steinicke F, Hinrichs KH. Stroke-based transfer function design. In : Proceedings of the Eurographics/IEEE VGC Workshop on Volume Graphics; 2008 Aug 10–11; Los Angeles, CA. p. 41–48.

19. Hadwiger M, Kratz A, Sigg C, Buhler K. GPU-accelerated deep shadow maps for direct volume rendering. In : Proceedings of the 21st ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware; 2006 Sep 3–4; Vienna, Austria. p. 49–52.

20. Marsalek L, Hauber A, Slusalled P. High-speed volume ray casting with CUDA. In : Proceedings of IEEE Symposium Interactive Ray Tracing; 2008 Aug 9–10; Los Angeles, CA.