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.2008 Dec;41(6):927-35.
doi: 10.1016/j.jbi.2008.02.008. Epub 2008 Mar 13.

Automatic perceptual color map generation for realistic volume visualization

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Automatic perceptual color map generation for realistic volume visualization

Jonathan C Silverstein et al. J Biomed Inform.2008 Dec.

Abstract

Advances in computed tomography imaging technology and inexpensive high performance computer graphics hardware are making high-resolution, full color (24-bit) volume visualizations commonplace. However, many of the color maps used in volume rendering provide questionable value in knowledge representation and are non-perceptual thus biasing data analysis or even obscuring information. These drawbacks, coupled with our need for realistic anatomical volume rendering for teaching and surgical planning, has motivated us to explore the auto-generation of color maps that combine natural colorization with the perceptual discriminating capacity of grayscale. As evidenced by the examples shown that have been created by the algorithm described, the merging of perceptually accurate and realistically colorized virtual anatomy appears to insightfully interpret and impartially enhance volume rendered patient data.

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Figures

Figure 1
Figure 1
Grayscale visualization viewed downward at the thoracic cavity in a bone window setting (−400 HU to 1000 HU). The heart is seen as a dominant feature in the lower left-center.
Figure 2
Figure 2
Identical visualization of the same dataset as in Figure 1, but using generic realistic color assignment defined by Table 1. The bronchia, diaphragm, heart, muscles and subcutaneous fat layer are more naturally delineated than in the grayscale colorization.
Figure 3
Figure 3
User interface for volume visualization with color map parameters set as used in Figure 2.
Figure 4
Figure 4
Generic RGB values for realistic coloring including interpolated regions from Table 1. The method for assigning colors is described in section 5.1. The blue line is a superimposed HU distribution from a 1324 axial slice high-resolution CT dataset. The air, fat, and soft tissue peaks are apparent and the bone region appears as a long tail 600 HU wide.
Figure 5
Figure 5
Y(HU) for perceptual grayscale and the generic versions of the realistic, spectral, and thermal color tables over the full CT data range (maximum HU window). Typically, the grayscale, spectral, and thermal tables dynamically scale with variable HU window widths, i.e., the shape of the Y(HU) plot remains the same but the window’s span of HU values determines the span of the plot. In contrast, realistic color schemes always map to the same exact HU values of the full HU window regardless of the selected window.
Figure 6
Figure 6
Comparison of generic and perceptual color maps applied to CT volume visualization of the left side view of the human heart along with bronchi, vertebrae, liver and diaphragm. (b), (d), (f) are the generic versions of the realistic, spectral, and thermal maps respectively. (a), (c), (e), (g) are perceptual versions of the grayscale, realistic, spectral, and thermal color maps respectively.
Figure 7
Figure 7
Volume rendering from a CT scan with vascular contrast showing the posterior view of the knee including the hamstrings, popliteal fossa, and gastrocnemius muscles. From left to right, (a) grayscale (perceptual), (b) generic realistic coloring, and (c) perceptual realistic coloring.
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References

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