Extending Measurement Science to Interactive 

Visualization Environments 

Judith Terrill1, William George1, Terence Griffin1, John Hagedorn1, John Kelso1, 

Marc Olano1

, Adele Peskin1, Steven Satterfield1, James Sims1, Jeffrey Bullard2, Joy 

Dunkers3, Nicos Martys2, Agnes O’Gallagher1, Gillian Haemer1

1

 Information Technology Laboratory, National Institute of Standards and Technology, 

Gaithersburg, MD 2

 Building and Fire Research Laboratory, National Institute of Standards and Technology, 

Gaithersburg, MD 3

 Material Science and Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, MD 

Abstract. We describe three classes of tools to turn visualizations into a visual laboratory to 

interactively measure and analyze scientific data. We move the normal activities that scientists 

perform to understand their data into the visualization environment, which becomes our virtual 

laboratory, combining the qualitative with the quantitative. We use representation, interactive 

selection, quantification, and display to add quantitative measurement methods, input tools, 

and output tools. These allow us to obtain numerical information from each visualization. The 

exact form that the tools take within each of our three categories depends on features present 

in the data, hence each is manifested differently in different situations. We illustrate the three 

approaches with a variety of case studies from immersive to desktop environments that demonstrate the methods used to obtain quantitative knowledge interactively from visual objects. 

Keywords: Measurement, Metrology, Visualization, Immersive visualization 

1 Introduction 

The National Institute of Standards and Technology (NIST) was established in 1901 

as the National Bureau of Standards. Although NIST's mission has expanded over 

the years, metrology, or measurement science, has remained a central theme. Measurement science has traditionally meant measurements on laboratory data that are 

made during the course of an experiment. However it is not always possible to perform desired measurements on laboratory data while the physical experiment is taking place. For example, a reconstruction phase may be needed to get the data into a 

form where it can be measured. An example is tomographic reconstruction, for 

which a great deal of information in the form of image data has to be carefully filtered, transformed, etc. to yield useful quantitative information. In addition laboratory experiments today are increasingly being replaced by computational experiments. Here, it may be too time consuming, expensive or impossible to do the laboratory experiment, and while quantitative data is typically gathered during numerical 
 Judith Terrill et al. 

simulations, there are also many reasons why additional measurements may need to 

be taken during a post-processing stage. Measurements and analyses from within 

interactive visualization environments provide unique capabilities in this regard. 

There are a variety of other motivations beyond pure measurement for adding interactive analysis to visualizations. Computational and laboratory experiments are 

generating increasing amounts of scientific data, and often, the complexity of the 

data makes it difficult to devise a priori methods for its analysis. Also, data currently 

is derived from new landscapes, such as the nanoworld, where we have little experience. There may be ancillary data, from databases for example, that may be required 

for understanding. Finally, just having the capability at hand to quantify the visualization speeds understanding. 

Most visualization techniques excel at providing a qualitative view of the visualized data. We are developing interactive measurement and analysis capabilities in 

desktop, three dimensional (3D) desktop, and immersive visualization environments, 

that bring quantitative data measurement into traditional qualitative visualization. 

We classify quantitative tools for visualization into three categories as follows: 

Quantitative Measurement: These are of two types: 1) interactive measurement 

tools analogous to physical measurement tools such as tape measures, and 

generalizations thereof; and 2) interactive tools to measure more abstract 

properties of data 

Quantitative Output: These are tools to perform interactive analyses and represent their output as quantitative data alongside raw data as it is visualized, 

and 

Quantitative Input: These are tools using values or graphs as part of an interactive interface to help drive the direction of a visualization (visualization 

steering) 

With real time qualitative and quantitative visual exploration and interaction, scientists can easily perceive complex relationships in their data, quickly ascertaining 

whether the results match expectations, and often obtain unexpected answers from 

visual observation. To support our classification for quantitative visualization tools, 

this chapter presents three case studies of visualizations at NIST incorporating quantitative tools for use by the domain scientist. In each of these three, the domain scientists played central roles, collaborating with computer scientists to develop visualizations and methods of interaction that produced valuable scientific measurements and 

insight. 

2 Related Work 

This work is not the first attempt to perform measurements in visualization environments (Brady, Pixton, Baxter, Moran, Potter, Carragher, and Belmont 1995; Bethel, 

Bastacky, and Schwartz 2002; Hastreiter, Rezk-Salama, Tomandl, Eberhardt, and 

Ertl 1998; Kim, Milgram, and Drake 1997; Preim, Teitjen, Spindler, and Peitgen 

2002, Preim and Bartz 2007). The most relevant work within immersive visualization environments is described by Reitinger, et al. who present a set of virtual reality 
 Extending Measurement Science to Interactive Visualization Environments 

tools for making measurements (distance, angle, and volume) for surgical planning 

(Reitinger, Schmalstieg, Bornick, and Beichel 2006). They point out that the virtual 

environment affords more natural interactions, so the user is able to make measurements more effectively than could be done on a non-interactive system, and this has 

certainly been our experience. Our work involves the implementation of some similar measurement tools, as well as some novel measurement tools, and incorporates 

interactive tools for statistical analyses and investigation of the measurement data 

derived from the 3D scene. Moreover, our toolbox is not specific to any one application; the tools are general and can be moved from application to application. 

On the commercial side, simple measurement capability has begun to appear in a 

variety of software e.g. Acrobat 3D (Adobe, 2007), and Amira (Mercury Computer 

Systems, 2007). On the desktop, measurement of 3D objects requires the ability to 

directly access any point in 3D space. This 3D desktop capability has existed for 

years in CAD software, but CAD systems are design tools – the scales of interest are 

an integral part of the design. In scientific visualization, the investigative scales generally come out of the data, and are not usually designed. This chapter will not address how to build a 3D desktop but rather assumes it exists and describes analytical 

techniques to gather and understand data in the 3D world. Immersive environments 

by their nature allow access to any part of space. So measurement in these environments is straightforward. We design our applications to work both on the desktop 

and immersively. 

3 Case Studies 

A few widely varying examples of the use of a 3D visualization environment to 

gather new information from data are described below. Each uses one or more of our 

three types of quantitative techniques that add to the productivity of understanding 

and quantifying that particular application. 

3.1 Tissue Engineering 

Tissue engineering has been identified by NIST as a biotechnology area in which 

metrological analyses are required to lower the cost barrier for product commercialization. To this end, NIST is developing methods to measure tissue engineered scaffold structures and analyzing scaffolds interactively. Scaffold structure is known to 

have a large influence on cell response, and this in turn affects the success of the 

final tissue engineered product. These structures are manufactured and then scanned 

using X-ray microcomputed tomography. The 3D structure is then reconstructed 

mathematically. Thresholding enables extraction of the scaffold which is visualized 

using a polygon approximation. We study the visualization with quantitative methods. 

For tissue engineering, we use all three classes of quantitative visualization tools 

within an immersive visualization environment to determine the fidelity of a manufactured tissue engineered scaffold to a design specification (Hagedorn, Dunkers, 

Satterfield, Peskin, Kelso, and Terrill 2007). Each of the quantitative measurement 
 Judith Terrill et al. 

tools places measurement objects into the virtual world that are, in some sense, surrogates for the features being measured. The user interacts with these objects in the 

immersive environment with a 3D cursor that is controlled by the position of the 

hand-held motion-tracked wand. The researcher can position and stretch those surrogate objects to fit the features of the data representations under study. The measurement of those features can then be taken from the known dimensions of the surrogates, and the user is able to analyze and interact with those measurements. So far, 

we have developed three such measurement tools: linear, cylindrical, and ellipsoidal. 

We envision these tools as the first of a set that will form a general purpose tool-box 

for the measurement of dimensional quantities in a virtual laboratory. 

The linear measurement tool lets the user stretch line segments to measure the 

linear distance between any two points. This is a virtual tape measure that provides a 

continuous numeric display of the length while the user is stretching each line segment. The cylinder measurement tool places wire-frame cylinders into the scene 

which the researcher can move, orient, and stretch in any dimension in order to 

match tubular features in the scene. The resulting dimensions of the wireframe cylinders can then be recorded to measure these features of the scene. Similarly, the ellipsoidal measurement tool enables the researcher to place wire-frame spheres into the 

scene. These spheres can then be interactively stretched into any ellipsoidal form and 

moved and oriented to match features of the scene. The various quantitative features 

of each ellipsoid can be recorded to measure these features. 

Fig. 1. Cylinder measurement and analysis tool used on rendered objects derived from 

measured data taken from a tissue engineering scaffold. 
 Extending Measurement Science to Interactive Visualization Environments 

All these positioning and stretching interactions are direct manipulations of the 

objects. For example, the user manipulates a linear measurement by grabbing one 

end of the line segment (point and click) and moving it to a new location (drag and 

drop). During the interaction, the system shows a continuous display of the rubberband line segment, with a continually updated numeric display of its length. 

As these linear, cylindrical, or ellipsoidal measurements are taking place, a second set of quantitative output tools allows the scientist to analyze the compiled 

measurements graphically, while they are being gathered. At any time, the user can 

display a histogram of the current set of measurements together with their mean and 

standard deviation. Finally, the histogram can be used as a quantitative input tool, 

allowing the user to sweep out a portion of the histogram and have the system highlight the corresponding line segment measurements whose lengths lie in that portion 

of the histogram. 

This closes the loop between measurement, analysis, and visualization. Here it 

lets the researcher see the relationship between the spatial location of measurements 

and their positions in the distribution of measurements. For example, we are able to 

highlight and examine all fibers whose diameters are less than 30% of the design size 

to see whether they are spatially clustered within the scaffold. Note also that the 

highlighting based on interaction with the histogram could be regarded as a form of 

brushing (Becker and Cleveland 1987). In the immersive system, this interaction 

takes place through a panel that is an element in the 3D virtual scene. Fig. 1 shows 

the cylindrical measurement tool with its accompanying analysis panel as it might 

appear in the immersive visualization environment. 

We performed linear measurements both on the “as-designed” scaffold model 

(generated synthetically from the design) and on the image of the actual manufactured scaffold material in the visualization environment. The former are intended to 

validate the measurement method, while the latter are used to understand the scaffold 

structural characteristics and fabrication method. We derived descriptors such as gap 

width, spacing of fibers, angles between intersecting struts, etc., and compared the 

results to those from the as-designed scaffold. We found that the inter-junction strut 

diameter was about 19% smaller than the as-designed model and the at-junction strut 

diameter (or layer thickness) was about 33% smaller than the as-designed model. 

However the angular measurements correspond very closely to the design (Hagedorn, Dunkers, Peskin, Kelso, Henderson, and Terrill 2006). 

The manual nature of these measurement tools presents some ease-of-use issues 

for the user. For example, when making large numbers of measurements in the immersive environment, user fatigue becomes a substantial issue. We also found that it 

was often difficult to align the surrogate objects with the surfaces that represent the 

objects being measured. The steadiness of the hand became a factor and many small 

adjustments were often required to get the desired result. This alignment problem 

was ameliorated in the immersive environment by interactively enlarging the virtual 

scene so that small mis-positionings would have less impact. However, it is also important to keep in mind that the (typically polygonal) representations that are displayed in the virtual environment are themselves only approximations of the real 

objects being measured. The researcher may choose not to place a surrogate object 
 Judith Terrill et al. 

exactly aligned with the data representation based on his or her scientific judgment 

about how the virtual object approximates the real object. 

Researchers reported that interactive measurement of the data provides valuable 

quantitative information for the scaffold manufacturers and scientists. They were 

able to look at multiple attributes at the same time, enabling the identification of 

manufacturing defects. The ability to interactively make physical measurements of 

objects in the virtual world and to display and interact with statistics derived from 

these measurements has let researchers efficiently collect and understand the data 

that they need. These domain scientists identified several areas in which this work 

could be extended: the integration of a fluid flow simulation to quantitatively understand nutrient transport issues in the scaffold; the imaging of cells growing on the 

scaffold structure; and inclusion of automated measurement techniques used side-byside with the manual measurement methods described above. 

3.2 Flow of Suspensions 

This research is focused on advancing our fundamental understanding of the 

rheological (flow) properties of suspensions with application to cement based materials. We are exploring how particle shape and size, distribution, and inter-particle 

forces and stresses affect the properties of suspensions. We simulate flows of both 

spheres and more realistic particle shapes using a Dissipative Particle Dynamics 

computational model that includes hydrodynamics, lubrication forces, interparticle 

forces and takes into account the local surface curvature (Martys, George, and 

Lootens 2007). In the 3D environment, we create both quantitative input and quantitative output visualization tools to analyze computational output. We use analysis 

tools to display and embed quantitative data interactively as it is investigated. Visual 

clues point out important aspects of data from which quantitative information can be 

derived. 

Visualization of Stress in Flows of Spherical Objects 

The jamming of suspensions is the subject of intense fundamental research. In particular, its understanding is useful for many applications including flow and placement of concrete and other granular materials. In this visualization, the goal is to 

understand the stress field in flows of spheres. We simulate a layer of spheres in suspension, where the bottom of the layer is fixed and the top of the layer undergoes a 

constant shearing force. Over the course of the simulation, the spheres participate in 

multiple small low-stress interactions with neighboring spheres. Our scientist collaborator hypothesized that when the system jams, one or more high-stress chains 

would form from sphere to sphere to sphere through the system, possibly dissipating 

quickly as the system breaks free. 

In order to see the stress field, we create a visual representation of the stress 

value between each pair of spheres (an output of the simulation). As shown in Fig. 2, 

the stress between each pair of interacting spheres is represented as a line. The 

sphere positions are implied from the line end points. We do not visualize the actual 
 Extending Measurement Science to Interactive Visualization Environments 

spheres because with their simple, known shape, they would just obscure the important data in the visualization. 3D time sequence animation allows a qualitative understanding of the system stress. As can be seen from the representation of stress in the 

figure, patterns of the stress can be ascertained visually, illuminating the directions 

along which the stress is being transferred through the system. The stress chains of 

the system are of significant importance to the researchers. This quantitative information embedded in our data makes these chains explicit to the scientist during the 

visualization. 

A slider selecting the upper and lower stress values to display gives real time 

quantitative interaction with the data. This provides the researchers with an interactive tool to visually explore the data by dynamically viewing the stress data through 

a data window defined by the slider controlled stress range, and allows the user to 

visually remove the data of low interest and focus on the areas where stress chains 

and potential jamming/locking occurs. 

An example of an identified stress line propagating from top to middle to bottom 

of the system is shown in Fig. 2. Another slider provides control of the animation 

Fig. 2. Stress in flows of spherical objects. The spheres are not shown, but are 

centered at the endpoints of each line segment. Stress chains show the structure of 

the stress field.
 Judith Terrill et al. 

speed. When a stress chain in the system occurs, it typically exists for a short period 

of time. A higher animation speed allows for a quick overview of the system. Reducing the animation to very slow or to a full stop allows an identified area and time step 

to be studied in more careful detail. The capability to focus on particular lines of 

stress and study them in frame-by-frame detail provides information to the scientist 

not otherwise available without these visible clues. 

Visualization of Stress in Flows of Realistic Shapes 

Flows of realistic random particulate shapes require new methods to obtain the qualitative and quantitative information needed by the scientists for a full understanding 

of the stress. The shape of the rocks, not just the distance between the rocks, influences stress chains and jamming. The physics of these flows requires different analytical tools than would be needed to study the flow of spheres. We therefore create a 

combination of our measurement techniques within the visualization environment. 

Fig. 3. Frame 63 in a simulation of the flow of rocks in a suspension. The per-rock stress is 

shown with a gray-yellow color scale, and also with a numeric value printed on the face for 

each rock. 
 Extending Measurement Science to Interactive Visualization Environments 

Hybrid Realistic / Non-Photorealistic Visualization 

We visualize the rocks directly, but augment the visualization with embedded data to 

provide additional information to the researcher on interactions between rocks and 

the per-rock stress (see Fig. 3). 

The addition of these values allows quantitative observations and measurement, 

as opposed to the purely qualitative insight provided by most visualizations. In addition, blue dots show the interaction points between rocks in the simulation. The system repeats toroidally, so blue points on the near side of the closest rocks correspond 

to interactions with rocks on the far side of the system. 

As the suspension becomes more and more dense, it becomes increasingly difficult to find the high-stress particles in a visualization, much less chains of such particles. While a smaller system may be simple enough to understand without further 

visualization tools, the larger system in Fig. 3 and our biggest system to date, 2025 

rocks, require extra visualization tools, such as culling the less interesting rocks, in 

order to allow the scientist to find potential jamming. 

Fig. 4. Frame 63 in a simulation of the flow of rocks in a suspension. Lower stress rocks are 

shown in sketch mode. 
 Judith Terrill et al. 

We gain inspiration from prior work in Focus+Context, primarily seen in information visualization (Jayaraman and North 2002; Pfitzner, Hobbs, and Powers 2003; 

Flider and Bailey 2004), and from non-photorealistic rendering in graphics (Feiner 

and Seligmann 1992; Markosian, Kowalski, Goldstein, Trychin, Hughes, and Bourdev 1997; Diepstraten, Weiskopf, and Ertl 2002). In this case, the scientist would 

like to focus on the highest stress rocks, but still have the indication of the other surrounding rocks to put the total system in context. To achieve this, we turn to nonphotorealistic rendering techniques to show these rocks in a sketchy style. The result, 

shown in Fig. 4, is quite effective, especially when seen in motion. The coherent 

motion of each low-stress rock's silhouette enhances the ability to recognize and distinguish individual rocks. In this visualization, we can clearly see a diagonal chain of 

rocks from the upper right, wrapping briefly to the opposite side of the simulation 

(recalling that the simulation wraps toroidally). 

In addition, it is useful to track the high-stress rocks from a single frame of the 

simulation through the entire simulation. That allows the scientist to see how those 

specific rocks move into gridlock, and how they escape it and continue on afterwards. For example, in some simulation runs, a small rock can be seen to move and 

twist to a new location while larger rocks remain locked in place, almost like workFig. 5. GUI for interactive exploration of the visualization. 
 Extending Measurement Science to Interactive Visualization Environments 

ing a 3D jigsaw puzzle. Then the larger rocks release, the stress goes down, and the 

aggregate motion continues. 

To assist the scientist in choosing a frame to display, we create a quantitative input visualization tool using a graph of the total system stress per frame as a form of 

slider (Fig. 5). The graph is generated in a preprocess using the statistical package R 

(R, 2007), and a selection bar is overlaid on top of it. The scientist can drag this selection bar to choose a frame. For example, frames just before a steep drop-off in 

stress are of particular interest, since they may indicate a time step when the rocks in 

the aggregate were binding, then one moved to release the pressure. Other interactions with the GUI in Fig. 5 enable toggling features of the visualization: closest 

point spots, stress colors, stress values, and solid/sketchy style transition value. 

The sparse visualization of Fig. 4 makes it much easier to see and understand the 

per rock digital stress measurements. The measurement value of total rock stress is 

centered on the face of the rock closest to the viewer, so as the system is rotated and 

manipulated, whether on the desktop or in the immersive visualization environment, 

the text always remains at the correct orientation. Some interactive rotation is generally necessary to see the values on hidden rocks, but with many fewer rocks displayed as solid photo-realistic entities, it is straightforward to find a suitable view to 

read the data from each rock. 

Shading-based visualization environment 

This set of visualizations is created with a procedural-shading based add-on to our 

visualization environment (Perlin 1985; Hanrahan and Lawson 1990; Corrie and 

Mackerras 1993). A shader is a short procedure which determines the color (i.e. 

shading) and opacity of each point on a surface. Our shaders are written in the 

OpenGL Shading Language (Rost 2005) and run in the graphics hardware at each 

pixel on each rock. This procedure determines the color and opacity of that pixel, 

referring to the current rock and frame number, position of the pixel on the rock, and 

data packed into a couple of floating point texture values. This procedure is responsible for creating the sketchy appearance for less important rocks, the stress color 

scale for solid rocks, placing the spots on the rock surface, and writing the numeric 

values on each rock. 

This shading framework allows a very flexible means to experiment with new 

visualization ideas. The shading procedure can be modified and reloaded while the 

application is running. It can use an arbitrary number of user-slider controllable parameters for interactive manipulation of the shader program behavior. For example, 

we can interactively change the specific frame used to decide which rocks are solid, 

and the stress level at which we show a blue closest point and the level to show a 

solid rock. 

Graphics hardware has a number of limitations that impact the shaders that can 

be created. Some, like the maximum available texture memory, are hard limits (512 

MB on our system), while others, like the number of texture accesses made by the 

shader, are soft limits affecting the frame rate and interactivity of the resulting visualization. For both reasons, we must process our sometimes multi-gigabyte raw data 
 Judith Terrill et al. 

into a compacted form to be stored in this limited texture memory for use by the 

shader program. We keep the total stress for each rock on each frame, as well as the 

closest point location and stress for the highest-stress closest points for each rock on 

each frame. In both cases, we need to turn a 3x3 stress tensor into a single scalar 

stress value for use and comparison, and combine these together to produce a single 

stress value per rock. There are two principal choices, referred to as shear stress and 

normal stress, although in some cases we have performed visualization using distance or log stress. Rather than make a single choice, our software takes a mathematical expression to guide the texture creation process. 

These visualization methods provide insight to scientists by combining several of 

our quantitative methods together enabling them to see spatial-temporal relationships. The scientists can find regions of interest concerning the stress between the 

rocks by visualizing lines of stress and embedded numerical values. They can 

graphically ask interactive questions and easily reduce their search for areas of important quantitative data. Our scientific collaborators report that the visualizations 

enable them to validate the physical correctness of the simulation, to detect problems, and to tune parameters of the model. The quantitative displays and interactions 

also enable the researchers to find and to focus on locations and periods of rapid 

change in stress. 

3.3 Cement Hydration 

Scientists can also use the 3D visualization environment to test the accuracy of their 

experimental methods, whether they are using computational or laboratory bench 

methods. This is an example of an application in which visualization was useful for 

debugging a complex numerical model. Both physical and graphical measurement 

tools can be used to understand the model and collect valuable numerical results. 

As a basic construction material, it is important to understand the complex properties of cement. Providing strength and structure to a large part of the man-made 

world, concrete has a complex mechanism for phase changes and development over 

time once it is poured. Models that can successfully predict the flow, hardening, and 

strength of concrete are currently being created. The Inorganic Materials Group 

within BFRL/NIST has new computational models for cement hydration that track 

both solvent and solute components whose concentrations vary over many orders of 

magnitude. Because the new software models equations of change that are extremely 

complex, it is important to have detailed visual output, both to test the accuracy of 

the models and to be able to understand the relative magnitudes of the various components across the volume of the system being studied. Precise quantitative visual 

information is required. 

To provide this kind of quantitative visualization, we can create a variety of different visual modes of output within a given visualization environment. A high concentration species within a given volume can be visualized by tracking a surface created from points at which the concentration of that component is at a designated isovalue. Components whose volume fractions change with time can be tracked by 

watching the decay or growth of a particular isosurface. Gradients of concentrations 
 Extending Measurement Science to Interactive Visualization Environments 

can be viewed by watching a series of semi-transparent isosurfaces, each representing a different isovalue of the component of interest. Time sequences of these isosurfaces provide clear visible information about the accuracy of the equations in the 

computations that produce the surfaces. We have developed an adaptive isosurface 

approach that reveals more features by changing the isosurface values over time 

based on characteristics of the data set. Low concentration ionic species can be 

tracked by looking at individual slices across the volume and watching the changes 

in isolines of the concentration of that ion. The scientist can zoom into a particular 

region of interest, for example, a seed point for the growth of a new species in the 

system, and carefully step through and watch the generation of that species. 

To provide more quantitative information, we provide a quantitative measurement tool, with which the scientist can interactively select particular regions of interest and query the precise concentrations of components in that region. 2D plots can 

be brought up in the visualization environment to show the volume fractions of each 

species at a particular point in space and time during the time analysis. In Fig 6, we 

see a 3D display of isosurfaces of the various components in the modeled system 

together with graphs depicting the volume fraction of each component and the values 

used for the adaptive isosurface display. 

The system allows for flexibility in choosing exactly which components and in 

which sub-volume the scientist would like to generate quantitative information. Testing the accuracy of the models and outputting this type of quantitative information 

are essential to the understanding of cement hydration. Because of the complexity of 

the system, quantitative visualization provides a creative way to obtain more inforFig. 6. Visualization of the cement hydration model with quantitative displays. 
 Judith Terrill et al. 

mation from the output of this model than purely qualitative methods. The ability of 

scientist to visually query locations of interest within the data and graphically examine those regions helps to narrow the search for important information. 

Researchers have reported that these methods provide insight into cement paste 

microstructure, particularly the 3D spatial relationships among the cementitious 

compounds. This enables the scientists to: validate the computational algorithms; to 

gain insight into the chemical relationships among phases (common reactants, nucleating agents, etc.) based on the spatial proximity that these phases have with each 

other; and to interpret macroscopic properties of cement in terms of changes in the 

three-dimensional microstructure. Future work could include the integration of interactive quantitative local chemical analysis, virtual microcalorimetry, the measurement of the thickness of cementitious compounds, and other numeric methods in 

the virtual environment. 

4 Summary 

Most visualizations excel at presenting a qualitative view of complex data, allowing 

scientists to quickly make overall judgments and locate interesting features in their 

data. The focus at NIST on measurement science has led us to introduce quantitative 

measurement tools into our visualizations. In this chapter, we present a classification 

of these tools into Quantitative Measurement, Quantitative Output, and Quantitative 

Input. Quantitative measurement tools allow direct in-visualization measurement of 

derived quantities such as lengths, volumes, etc. Quantitative output tools analyze 

the visualized data and present concrete values or graphs alongside the visualization 

or embedded in it. Quantitative input tools use direct, physically meaningful values 

or graphs as input to the visualization. These quantitative visualization tools can be 

used alone or together to enhance traditional qualitative visualizations. Through 

measurement, visualization can become a more effective instrument for quantitative 

science. 

This chapter also presents three case studies demonstrating the use of quantitative 

visualizations in collaboration with domain scientists. We present quantitative input 

and several forms of quantitative measurement for analyzing X-ray microcomputed 

tomography data of a tissue engineering scaffold. We show quantitative output and 

several types of quantitative input for flow of particles in suspension. Finally, we 

present quantitative output and quantitative measurement within a cement hydration 

simulation. These case studies have shown how the integration of these quantitative 

methods can direct scientists to otherwise unknown features of interest and then provide tools to single out and analyze those features. These tools allow scientists to 

view large amounts of numerical data from computational simulations and see subtle 

problems with the numerical calculations when resulting output has inconsistent features. They also provide data mining information on large data sets, assessing statistically important features in the data. 

Future work includes the implementation of additional quantitative interactive 

measurement and display tools, the application of these methods to non-spatial data 

(Devaney, Satterfield, Hagedorn, Kelso, Peskin, George, Griffin, Hung, and Kriz, 
 Extending Measurement Science to Interactive Visualization Environments 

2005), and development of methods for analysis and display of uncertainties in the 

input data and in our interactive measurements. We note that laboratory measurements on physical objects always have an uncertainty associated with them. We will 

be applying this same standard to measurements and analyses of virtual objects. 

Disclaimer 

Certain commercial equipment, instruments, or materials are identified in this 

paper to foster understanding. Such identification does not imply recommendation or 

endorsement by the National Institute of Standards and Technology, nor does it imply that the materials or equipment identified are necessarily the best available for 

the purpose. 

Acknowledgment 

The Flow of Suspensions computations were performed under Award SMD-05-

A-0129, "Modeling the Rheological Properties of Suspensions: Application to Cement Based Materials", for NASA's National Leadership Computing System initiative on the "Columbia" supercomputer at the NASA Ames Research Center. 

References 

Adobe Software: http://www.adobe.com

Becker, R.A. and Cleveland, W.S. (1987) Brushing scatterplots. Technometrics, 29(2):127–

142 

Bethel, E. W., Bastacky, S.J., and Schwartz, K.S. (2002) Interactive stereo electron microscopy enhanced with virtual reality. In: A. J. Woods, J. O. Merritt, S. A. Benton, M. T. Bolas, L. Spector, I. Parmee, and H.-G. Beyer (Eds.), Stereoscopic Displays and Virtual Reality Systems IX, SPIE, San Jose, CA, pp. 391–400. 

Brady, R., Pixton, J., Baxter, G., Moran, P., Potter, C.S., Carragher, B., and Belmont, A. 

(1995) Crumbs: a virtual environment tracking tool for biological imaging. In: Proceedings of IEEE Symposium on Frontiers in Biomedical Visualization. 

Corrie, B. and Mackerras, P. (1993) Data Shaders. In: Proceedings of the 4th conference on 

Visualization. pp. 275-282. 

Devaney, J., Satterfield, S., Hagedorn, J., Kelso, J., Peskin, A., George, W., Griffin, T., Hung, 

H., Kriz, R., Science at the Speed of Thought. In Ambient Intelligence for Scientific Discovery: Lecture Notes in Artificial Intelligence , 3345, (2005), pp. 1-24. 

Diepstraten, J., Weiskopf, D. and Ertl, (2002) T. Transparency in Interactive Technical Illustrations. Computer Graphics Forum 21(3), pp. 317-325. 

Elmqvist, N. and Tudoreanu, M.E. (2006) Evaluating the Effectiveness of Occlusion Reduction Techniques for 3D Virtual Environments. In: Proceedings of the ACM Symposium on 

Virtual Reality Software and Technology. ACM Press, New York, pp. 9-18. 

Feiner, S.K. and Seligmann, D.D. (1992) Cutaways and Ghosting: Satisfying Visibility Constraints in Dynamic 3D Illustrations. The Visual Computer. 8(5-6), pp. 292-302. 

Flider, M.J. and Bailey, B.P. (2004) An Evaluation of Techniques for Controlling Focus+Context Ccreens. In: Proceedings of Graphics Interface 2004. pp. 135-144. 
 Judith Terrill et al. 

Hagedorn, J., Dunkers, J., Peskin, A., Kelso, J., Henderson, L., and Terrill, J. (2006) Quantitative, interactive measurement of tissue engineering scaffold structure in an immersive 

visualization environment. In: Biomaterials Forum, Vol. 28, No. 4, pp. 6-9. 

Hagedorn, J., Dunkers, J., Satterfield, S., Peskin, A., Kelso, J., and Terrill, J. (2007) Measurement tools for the immersive visualization environment. In: Journal of Research of the 

National Institute of Standards and Technology, Vol. 112, No. 5, pp. 257-270. 

Hanrahan, P. and Lawson, J. (1990) A Language for Shading and Lighting Calculations. In: 

SIGGRAPH '90: Proceedings of the 17th Annual Conference on Computer Graphics and 

Interactive Techniques. ACM Press, New York, pp. 289-298. 

Hastreiter, P., Rezk-Salama, Ch., Tomandl B., Eberhardt, K.E.W., and Ertl T. (1998) Fast 

analysis of intracranial aneurysms based on interactive direct volume rendering and CTA. 

In W. M. Wells, A. Colchester, and S. Delp (Eds.), Medical Image Computing and Computer-Assisted Interventation – MICCAI’98, pp. 660–668. 

Jayaraman, S. and North, C. (2002) A Radial Focus+Context Visualization for MultiDimensional Functions. In: Proceedings of the Conference on Visualization '02.IEEE, 

Washington, DC, pp. 443-450. 

Kim, M., Milgram, P., and Drake, J. (1997) Virtual tape measure for 3D measurements in 

micro-surgery. In: Engineering in Medicine and Biology Society, Proceedings of the 19th 

Annual International Conference of the IEEE, pp. 967–969. 

Leung, Y.K. and Apperley, M.D. (1994) A Review and Taxonomy of Distortion-Oriented 

Presentation Techniques. ACM Trans. Comput.-Hum. Interact. 1(2), pp. 126-160. 

Markosian, L., Kowalski, M.A., Goldstein, D., Trychin, S.J., Hughes, J.F. and Bourdev, L.D. 

(1997) In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques. ACM Press, New York, pp. 415-420. 

Martys, N., George, W., Lootens, D. (2007) Spatial-temporal correlations in startup-up flows 

of colloidal suspensions. In preparation. 

Mercury Computer Systems: http://www.tgs.com

Perlin, K. (1985) An Image Synthesizer. In: Proceedings of the 12th Annual Conference on 

Computer Graphics and Interactive Techniques. ACM Press, New York, pp. 287-296. 

Pfitzner, D., Hobbs, V., Powers, D. (2003) A Unified Taxonomic Framework for Information 

Visualization. In: Proceedings of the Asia-Pacific Symposium on Information Visualisation. Australian Computer Society, Adelaide, Australia, pp. 57-66. 

Preim, B. and Bartz, D. (2007) Visualization in Medicine. Morgan Kaufmann, Burlington, 

MA, USA. 

Preim, B., Tietjen, C., Spindler, W., and Peitgen, H.-O. (2002) Integration of measurement 

tools in medical 3D visualizations. In: IEEE Visualization, pp. 21–28. 

R Package for Statistical Computing: http://www.r-project.org, 2007 

Reitinger, B., Schmalstieg, D., Bornik, A., and Beichel, R. (2006) Spatial analysis tools for 

virtual reality-based surgical planning. In: Proceedings of the 2006 IEEE Symposium on 

3D User Interfaces, pp. 37–44. 

Rost, R.J. (2005) OpenGL(R) Shading Language, 2nd Edition. Addison-Wesley Professional, 

Boston, MA, USA.