Efficient computation of cross-sections from human brain model by geometric processing

Abstract

In this paper, an efficient method for computing the cross-sections of the internal structure from a 3D human brain model has been proposed. It can extract image slices from the brain model in sagittal, coronal, and axial views used for computed tomography and ultrasonography. A doubly connected edge list (DCEL) has been used for speeding up the computation during geometric processing, since the DCEL captures the topological relationship among vertices, edges, and faces of the triangulated surface. For a sectional plane, image slices are computed quite efficiently using the information of geometric coherence from the previous sectional plane with the help of DCEL. The optimal distance between two successive sectional planes is determined from the frequency distribution (Poisson distribution) of the edge lengths in the model. It reduces computational overhead without compromising on the quality of output, as demonstrated by experimental results.