CCB Research

The main focus of the CCB is on the brain, and specifically on neuroimaging. This area has a long tradition of sophisticated mathematical and computational techniques. Nevertheless, new developments in related areas of mathematics and computational science have emerged in recent years, some from related application areas such as Computer Graphics, Computer Vision, and Image Processing, as well as from Computational Mathematics and the Computational Sciences. We are confident that many of these ideas can be applied beneficially to neuroimaging.

CCB Research Areas

Mathematics:
The mathematical research is focused on addressing fundamental problems in the computational modeling of biological structures. The solutions and techniques that we are proposing build on our recent advances in image analysis, differential equations, numerical analysis, and differential geometry, as well as our many years' work in mathematics and neuroscience. More

Modeling:
We have organized the modeling research to look at two areas of modeling techniques. The first area investigates surface modeling and conformal mapping, and the second area is focused on the invesitgation of image segmentation using level sets and geometrical PDEs. More

Biomedical Computing:
We are developing methods for interacting with data and algorithms. Included in these methods is the development of interactive visual environments for data processing. More

Data Analysis:
Our research in data analysis emphasizes the importance in bridging the mathematics we have developed in to biomedical research applications, and the delivery of the developed tools and techniques to the research community. More

Neurobiology:
The CCB neurobiological research is divided into a number of different individual research studies. These studies include the following: More

• Identifying Age Related Atrophy Using Levelset Registration of Embedded Maps
• Developmental origin of phenotypic variation in Drosophila melanogaster
• Mapping Brain Changes in HIV/AIDS
• Vervet Genetics and Brain Morphology

Shape Modeling and Analysis:
A complete description of a biological object (e.g., organ) is complemented by both shape features and shape attributes. A shape feature is any characterization of shape based on both local and global geometry as well as topology of the object. On the other hand, a shape attribute is any local or global information important to visualize the nature of the object, and to model the study or processing step required by the study. Examples of shape attributes we study include textures, colors, displacement vectors and other characteristics related to the biological object bound by the shape (e.g., functional data from fMRI, PET, MRI/DTI, cross-sectional and longitudinal data, lobular or sulcal/gyral labels).