Tagged: visualization

What is isosurfacing?

Isosurfacing is a visualization technique for extracting a surface from 3D volumetric data, such as a CT or MRI scan. The technique, first developed by researchers at GE called Marching Cubes,  essentially started the 3D medical visualization industry.

Image analysis is hard. To extract meaningful information to meet your analysis goals you often need to work with large, complex, multidimensional and multivariate data. I have found that the following methodology can be used to effectively design software to successfully analyze image data.

Fundamental methods for processing geometrical surfaces are commonplace in today’s visualization and analysis packages. Tools that provide fast and accurate manipulation of large surface meshes are de facto in this class of tools.

A commonly occurring problem where two or more data sets must be aligned to one another for the purpose of comparison, blending, visualization, and analysis. Rotating, scaling, and translating the 2D or 3D data sets usually accomplish this so that the each correspond to one another based upon some specified error metric.

When it comes to true accuracy in regards to the visualization of your data, you can only trust the robustness of ray tracing. These methods yield true quality images like those seen in Hollywood’s movies as well as extremely complex numerical simulations.

Choosing a visualization method that most easily and accurately conveys the meaning of data is not always a complex three-dimensional image. Sometimes, insightful meaning can most easily be conveyed by a simple graph and often interaction through these simple interfaces can greatly increase the understanding of complex data.

Emphasis on more subtle features of data often provides more insight than expected. Finding this balance between realism and subtle cues is a fine art and greatly depends on the data and what insight the viewer needs.

Bring new life to your three-dimensional volumetric data by viewing it in its natural form. Gain contextual insight by viewing transparent blends of your complex data. Data that typically benefits from volumetric methods are magnetic resonance imaging (MRI), computed tomography (CT), PET, etc.

Becoming more popular in the finite element field, higher-order elements capture a more complex data representation than their linear element predecessors and reduce the required number of elements needed to decompose fluid flow, stress, and other simulation types.