David E. Breen: Research Interests - Deformable Models

I have studied two types of deformable models for computer graphics. The goal of the first effort (conducted with James V. Miller) was to develop models capable of extracting a closed geometric polygonal model from a volume dataset. We were interested in generating a model that could be used not only for visualization, but also for geometric calculations. The general approach, which we called Geometrically Deformed Models (GDMs), involved placing a closed polygonal model with spherical topology within the volume datset. We then allowed the geometric model to deform in reponse to local expansion forces and topological constraints, while interacting with the volume dataset. As the model expanded or contracted the individual polygons of the model subdivided or combined in order to maintain faces with a specified area. Once the deformation process came to an equilibrium, a closed polygonal suface is produced which approximates a specific iso-surface running through the volume dataset. (See Miller et al. 1990 & 1991).

I am currently exploring level set methods for computer graphics, visualization, and computer animation. This is joint work with Ross Whitaker of the University of Utah. Our first project developed a technique for 3D metamorphosis (morphing). Our technique guarantees that one object will smoothly transform into another object as long as the two objects initially overlap. The advantage of our technique is that user input is not required in order to produce a reasonable morphing result. Additional user input may be incorporated into the technique in order to provide user control of the morphing process. (See Breen et al. 2001, Breen & Whitaker 2001, and Whitaker & Breen 1998).

In our second joint project, we are developing a level-set framework for segmenting models from volumetric data. The framework consist of a variety of initialization methods that may be combined with level set deformation in order to extract closed, smooth structures from many types of volumetric datasets. (See Whitaker et al. 2001). For example, the framework has been used to extract the organs from a 12-day-old mouse embryo MRI scan, the structures of a developing frog embryo from time sequence MRI scans, a spiny dendrite from an electron tomogram, and the anistropic diffusion region from a diffusion tensor scan (see Zhukov et al. 2001). The framework is currently being encapsulated in a number of Iris Explorer modules that will provide an extensible, and easy-to-use interface.

Research Results

• GDMs

• A 2D GDM finding the boundary of a chest cavity
  Image generated by James V. Miller, GE CR&D.

• A 3D GDM extracting a nerve from a tooth MRI scan
  Image generated by James V. Miller, GE CR&D.

• A 3D GDM extracting a cooling chamber from a turbine blade MRI scan
  Image generated by James V. Miller, GE CR&D.

• 3D MORPHING

• Morphing Animations
• More Morphing Animations

• Morphing a Dart into a Jet

• Another Dart-to-Jet Morph with Different Initial Conditions

• Morphing a Dart into a Jet with Interpolating Surface Colors

• Morphing a Mug into a Chain

• Morphing Between Different Kinds of Head Models

• SEGMENTATION

• Mouse Embryo Segmentation

• Frog Embryo MRI Sequence Segmentation

• Regions with Anisotropic Diffusion in a Human Brain

• Same Regions After Applying Level Set Smoothing

• Relationship of Ventricles to Anisotropic Regions in a Human Brain (Side)

• Relationship of Ventricles to Anisotropic Regions in a Human Brain (Bottom)

• Regions with Anisotropic Diffusion in a(nother) Human Brain. Raw data -> initialization -> level set smoothing.