Visualization in its broadest terms represents any technique for creating images to represent abstract data. Scientific Visualization has grown to encompass many other areas like business (information visualization), computing (process visualization), medicine, chemical engineering, flight simulation, and architecture. Actually there’s not a single area of human endeavor that does not fall under scientific visualization in one form or another.
From a crude perspective, scientific visualization was born out of the conversion of text into graphics. For instance, describing an apple with words. Bar graphs, charts and diagrams were a 2-dimensional forerunner in converting data into a visual representation. Obviously words and 2-dimensional representations can only go so far, and the need for more mathematically accurate datasets was needed to describe an object’s exterior, interior, and functioning processes.
Such datasets were huge, and it wasn’t until the development of supercomputers with immense processing power combined with sophisticated digital graphics workstations that conversion from data into a more dynamic, 3-D graphical representation was possible. From the early days of computer graphics, users saw the potential of computer visualization to investigate and explain physical phenomena and processes, from repairing space vehicles to chaining molecules together.
In general the term “scientific visualization” is used to refer to any technique involving the transformation of data into visual information. It characterizes the technology of using computer graphics techniques to explore results from numerical analysis and extract meaning from complex, mostly multi-dimensional data sets.
Traditionally, the visualization process consists of filtering raw data to select a desired resolution and region of interest, mapping that result into a graphical form, and producing an image, animation, or other visual product. The result is evaluated, the visualization parameters modified, and the process run again.
Three-dimensional imaging of medical datasets was introduced after clinical CT (Computed axial tomography) scanning became a reality in the 1970s. The CT scan processes images of the internals of an object by obtaining a series of two-dimensional x-ray axial images.
The individual x-ray axial slice images are taken using a x-ray tube that rotates around the object, taking many scans as the object is gradually passed through a tube. The multiple scans from each 360 degree sweep are then processed to produce a single cross-section. See MRI and CAT scanning in the Optics section.
The goal in the visualization process is to generate visually understandable images from abstract data. Several steps must be done during the generation process. These steps are arranged in the so called Visualization Pipeline.
Data is obtained either by sampling or measuring, or by executing a computational model. Filtering is a step which pre-processes the raw data and extracts information which is to be used in the mapping step. Filtering includes operations like interpolating missing data, or reducing the amount of data. It can also involve smoothing the data and removing errors from the data set.
Mapping is the main core of the visualization process. It uses the pre-processed filtered data to transform it into 2D or 3D geometric primitives with appropriate attributes like color or opacity. The mapping process is very important for the later visual representation of the data. Rendering generates the image by using the geometric primitives from the mapping process to generate the output image. There are number of different filtering, mapping and rendering methods used in the visualization process.
Some of the earliest medical visualizations, created 3D representations from CT scans with help from electron microscopy. Images were geometrical shapes like polygons and lines creating a wire frame, representing three-dimensional volumetric objects. Similar techniques are used in creating animation for Hollywood films. With sophisticated rendering capability, motion could be added to the wired model illustrating such processes as blood flow, or fluid dynamics in chemical and physical engineering.
The development of integrated software environments took visualization to new levels. Some of the systems developed during the 80s include IBM’s Data Explorer, Ohio State University’s apE, Wavefront’s Advanced Visualizer, SGI’s IRIS Explorer, Stardent’s AVS and Wavefront’s Data Visualizer, Khoros (University of New Mexico), and PV-WAVE (Precision Visuals’ Workstation Analysis and Visualization Environment).
These visualization systems were designed to help scientists, who often knew little about how graphics are generated. The most usable systems used an interface. Software modules were developed independently, with standardized inputs and outputs, and were visually linked together in a pipeline. These interface systems are sometimes called modular visualization environments (MVEs).
MVEs allowed the user to create visualizations by selecting program modules from a library and specifying the flow of data between modules using an interactive graphical networking or mapping environment. Maps or networks could be saved for later recall.
General classes of modules included:
• data readers – input the data from the data source
• data filters – convert the data from a simulation or other source into another form which is more informative or less voluminous
• data mappers – convert information into another domain, such as 2D or 3D geometry or sound
• viewers or renderers – rendering the 2D and 3D data as images
• control structures – display devices, recording devices, open graphics windows
• data writers – output the original or filtered data
MVEs required no graphics expertise, allowed for rapid prototyping and interactive modifications, promoted code reuse, allowed new modules to be created and allowed computations to be distributed across machines, networks and platforms.
Earlier systems were not always good performers, especially on larger datasets. Imaging was poor.
Newer visualization systems came out of the commercial animation software industry. The Wavefront Advanced Visualizer was a modeling, animation and rendering package which provided an environment for interactive construction of models, camera motion, rendering and animation without any programming. The user could use many supplied modeling primitives and model deformations, create surface properties, adjust lighting, create and preview model and camera motions, do high quality rendering, and save images to video tape.
Acquiring data is accomplished in a variety of ways: CT scans, MRI scans, ultrasound, confocal microscopy, computational fluid dynamics, and remote sensing. Remote sensing involves gathering data and information about the physical “world” by detecting and measuring phenomena such as radiation, particles, and fields associated with objects located beyond the immediate vicinity of a sensing device(s). It is most often used to acquire and interpret geospatial data for features, objects, and classes on the Earth’s land surface, oceans, atmosphere, and in outerspace for mapping the exteriors of planets, stars and galaxies. Data is also obtained via aerial photography, spectroscopy, radar, radiometry and other sensor technologies.
Another major approach to 3D visualization is Volume Rendering. Volume rendering allows the display of information throughout a 3D data set, not just on the surface. Pixar Animation, a spin-off from George Lukas’s Industrial, Light and Magic (ILM) created a volume rendering method, or algorithm, that used independent 3D cells within the volume, called “voxels”.
The volume was composed of voxels that each had the same property, such as density. A surface would occur between groups of voxels with two different values. The algorithm used color and intensity values from the original scans and gradients obtained from the density values to compute the 3D solid. Other approaches include ray-tracing and splatting.
Scientific visualization draws from many disciplines such as computer graphics, image processing, art, graphic design, human-computer interface (HCI), cognition, and perception. The Fine Arts are extremely useful to Scientific Visualization. Art history can help to gain insights into visual form as well as imagining scenarios that have little or no data backup. Along with all the uses for a computer an important part of the computers future is the invention of the LCD screens, which helped tie it all together. This brought the visual graphics to life, with better resolution, lighter weight and faster processing of data than the computer monitors of the past.
Computer simulations have become a useful part of modeling natural systems in physics, chemistry and biology, human systems in economics and social science, and engineering new technology. Simulations have rendered mathematical models into visual representations easier to understand. Computer models can be classified as Stochastic or deterministic.
Stochastic models use random number generators to model the chance or random events, such as genetic drift. A discrete event simulation (DE) manages events in time. Most simulations are of this type. A continuous simulation uses differential equations (either partial or ordinary), implemented numerically. The simulation program solves all the equations periodically, and uses the numbers to change the state and output of the simulation. Most flight and racing-car simulations are of this type, as well as simulated electrical circuits.
Other methods include agent-based simulation. In agent-based simulation, the individual entities (such as molecules, cells, trees or consumers) in the model are represented directly (rather than by their density or concentration) and possess an internal state and set of behaviors or rules which determine how the agent’s state is updated from one time-step to the next.
Winter Simulation Conference
The Winter Simulation Conference is an important annual event covering leading-edge developments in simulation analysis and modeling methodology. Areas covered include agent-based modeling, business process reengineering, computer and communication systems, construction engineering and project management, education, healthcare, homeland security, logistics, transportation, distribution, manufacturing, military operations, risk analysis, virtual reality, web-enabled simulation, and the future of simulation. The WSC provides educational opportunity for both novices and experts.
Ohio State University, Department of Design http://design.osu.edu/carlson/history/lesson18.html