Brain segmentation and the generation of cortical surfaces

Mukta Joshi, Jing Cui, Keith Doolittle, Sarang Joshi, David Van Essen, Lei Wang, Michael I. Miller

Research output: Contribution to journalArticlepeer-review

70 Scopus citations


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.

Original languageEnglish
Pages (from-to)461-476
Number of pages16
Issue number5
StatePublished - May 1999


Dive into the research topics of 'Brain segmentation and the generation of cortical surfaces'. Together they form a unique fingerprint.

Cite this