Introduction
Scientific Visualization is the visualization of 3-Dimensional phenomena (medical, biological, metereological, etc..) with the purpose to render meaningful volumes and surfaces. An area of scientific visualization that is very common and useful is medical imaging. The data collected from a tomographic system such as a CT or X-ray system consists of a set of parallel planes that can be viewed as a 3D volume once all the planes are put together in a pre-defined sequence as shown in figure 1. Each of these planes contains a particular property such as density, color or X-ray absorption coefficient that was captured at an instant in time.
In the past few years, advancements in hardware and software has made it feasible to render the large volumetric datasets acquired by these scanning devices since these datasets can range from few megabytes to a several gigabytes in size depending on the resolution. An example is the Visible Human Project which consists of MRI, X-ray CT and anatomical images obtained from cadavers. Each of the datasets, male and female cadavers, contains 15 gigabytes and 40 gigabytes of voxel data sets, respectively. In trying to visualize these large volumetric datasets, each property value can be represented as an individual volumetric unit, or voxel. Each voxel encapsulates several properties such as (x,y,z) location, density, color, gradient, normal, etc... as shown in figure 2.
In trying to visualize these volumetric datasets, several techniques exist which allows for the extraction of meaningful information. This meaningful information must only contain the relevant data while ignoring all other data. For example, a CT scan of a baby head must only show the head while trying to avoid the respiratory tube and head padding as well as other artifacts. This is very important in order to allow the clinician to efficiently and precisely diagnose and screen for disease.
Goals
The main goals of this project are to learn about the techniques commonly used to visualize CT or MRI data and to be exposed to the concepts of volume rendering. Understanding these techniques and implementing some of them will prove useful in learning the pros and cons of these techniques as well as the ideas and terminology currently being used in the medical field of scientific visualization.In order to accomplish these main goals, I decided to implement the following techniques and components that are essential to volume rendering. These goals include:
1. Understand the scientific datasets and extract the data
2. Render the dataset using the "Bubble Model" technique
3. Render the dataset using Ray Casting for surface shading
4. Create a Graphical User Interface (GUI) to make it interactive