Brain segmentation and the generation of cortical surfaces

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

70 Scopus citations

Abstract

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 (US)
Pages (from-to)461-476
Number of pages16
JournalNeuroimage
Volume9
Issue number5
DOIs
StatePublished - May 1999

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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