Brain surface analysis

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Project Team

  • Dr. Francis Quek Professor, Center for HCI
  • Cemil Kirbas Ph.D. Student - graduated
  • Richard Yarger M.S. Student - graduated

Note: This is a past project that is currently dormant

Brief Description

In this research, we employed deformable models to extract the surface contours in 3D medical images. By a first order estimation of local surface normals, we regroup the surface points into Monge patches that allow us to compute analytical surface models everywhere using a variant of the Facet Model. We can then characterize the brain surface in terms of 3D curvature patches that help to identify critical brain surface regions.


  • Quek, F., Yarger, R., and Kirbas, C., “Surface Parameterization in Volumetric Images for Curvature-Based Feature Classification,” IEEE Transactions on Systems, Man, and Cybernetics. Vol. 33, No. 5, October, 2003, pp. 758-765.
  • Quek, F., Kulkarni, V., and Kirbas, C., “Comparison of Bicubic and Bèzier Polynomials for Surface Parameterization in Volumetric Images,” IEEE Conference Bio-Informatics and Bio-Engineering (BIBE), 2003, pp. 107-114.
  • Yarger, R., and Quek, F., “Surface Parameterization in Volumetric Images for Feature Classification,” IEEE International Symposium of Bio-Informatics and Bio-Engineering (BIBE) 2000. pp. 297—303, Washington D.C., Nov. 8-10, 2000.


This research was supported by the Whittaker Foundation: “Extraction and Registration of the Neurovascular Scaffold in Multimodal Images,” Whitaker-96-0458