Brain segmentation and the generation of cortical surfaces.
This paper describes methods for white matter segmentation in brain images and the generation of cortical surfaces from the segmentations. We have developed a system that allows a user to start with a brain volume, obtained by modalities such as MRI or cryosection, and constructs a complete digital representation of the cortical surface. The methodology consists of three basic components: local parametric modeling and Bayesian segmentation; surface generation and local quadratic coordinate fitting; and surface editing. Segmentations are computed by parametrically fitting known density functions to the histogram of the image using the expectation maximization algorithm [DLR77]. The parametric fits are obtained locally rather than globally over the whole volume to overcome local variations in gray levels. To represent the boundary of the gray and white matter we use triangulated meshes generated using isosurface generation algorithms [GH95]. A complete system of local parametric quadratic charts [JWM+95] is superimposed on the triangulated graph to facilitate smoothing and geodesic curve tracking. Algorithms for surface editing include extraction of the largest closed surface. Results for several macaque brains are presented comparing automated and hand surface generation.
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology*; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D006801:Humans; D016013:Likelihood Functions; D008251:Macaca; D008279:Magnetic Resonance Imaging; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013499:Surface Properties