11th Annual OHBM Meeting  
 
Abstract Number: 640 
Submitted By: Lee Singleton  
Last Modified: 11 Jan 05 
^{}Department of Mathematics, Florida State University, Tallahassee, FL 323064510  
Objective: Cortical
surface reconstructions are increasingly being used to study
the anatomy and functional processing of the brain and to make
comparisons across individuals. Although the geometry of the
surface of the brain varys across individuals, its topology is
always equivalent to a twosphere with an Euler characteristic
(c) of 2. Many different algorithms
exist for reconstructing cortical surfaces. We use the
marching cubes algorithm^{[1]}, one of the most common
algorithms used for constructing a surface from volume data,
namely because of its speed. We use variations in this
algorithm to compare geometric and topological properties in
the resulting cortical surface. To our knowledge, this is the
first comparison of geometric and topological cortical surface
properties to be performed with the marching cubes
algorithm.
Methods: The marching cubes algorithm and its variations rely on a lookup table to reconstruct a triangulated surface corresponding to a given value known as the isovalue. The properties of the triangulated brain surface can be quite different, depending on the triangulation premise^{[2][3]} and isovalue chosen. In order to facilitate more efficiency in downstream applications such as handle removal and flattening, we wish to optimize the surface reconstruction algorithm as much as possible. We have compared the effects of using different lookup tables and isovalues, as well as perturbing data values that are equal to the isovalue, to find the best combination of properties on MRI data from 11 different subjects. Some important surface characteristics we are investigating include the number of generated triangles, surface area, c, and the maximum degree of the vertices (largest number of triangles at one vertex). A smaller maximum degree is desired since fewer triangles at a single vertex result in fewer problems in later applications. A smaller number of triangles in the surface will mean a smaller number of processing steps further downstream. Results & Discussion: A subset of our results is shown in Table 1. Conclusions: The manner in which the marching cubes algorithm is run does have an effect on the resulting surface. The low separation table and asymptotic table result in the best combination of triangles, c, and vertex degree. However, using an asymptotic decider with marching cubes has a slightly longer computational time and has varying c results, depending on the isovalue used. Using integer isovalues and perturbing data values equal to the isovalue also gives better results than using nonintegers for the isovalue. In the future, we will investigate new algorithms of data perturbation in order to give topologically correct surfaces. References & Acknowledgements: [1] W. Lorensen and H. Cline. Marching Cubes: a high resolution 3D surface construction algorithm. Computer Graphics, 21(4):163170, 1987. [2] G. Nielson and B. Hamann. The Asymptotic decider: resolving the ambiguity in marching cubes. Proceedings of Visualization, 29–38, 1991. [3] T. Lewiner, et. al. Efficient implementation of marching cubes' cases with topological guarantees. J.Graphics Tools, 8(2):115, 2003. This work is supported by grants NSF:DMS0101329, NIH:P20EB02013. We thank Dr. David Rottenberg, Radiology and Neurology Departments, U.Minnesota for the MRI data.
