Using a statistical shape model to extract sulcal curves on the outer cortex of the human brain

Xiaodong Tao, Jerry L. Prince, Christos Davatzikos

Research output: Contribution to journalConference article

Abstract

A method for automated segmentation of major cortical sulci on the outer brain boundary is presented, with emphasis on automatically determining point correspondence and on labeling cortical regions. The method is formulated in a general optimization framework defined on the unit sphere, which serves as parametric domain for convoluted surfaces of spherical topology. A statistical shape model, which includes a network of deformable curves on the unit sphere, seeks geometric features such as high curvature regions and labels such features via a deformation process that is confined within a spherical map of the outer brain boundary. The limitations of the customary spherical coordinate system, which include discontinuities at the poles and nonuniform sampling, are overcome by defining the statistical prior of shape variation in terms of projections of landmark points onto corresponding tangent planes of the sphere. The method is tested against and shown to be as accurate as manually defined segmentations.

Original languageEnglish (US)
Pages (from-to)513-524
Number of pages12
JournalIEEE transactions on medical imaging
Volume21
Issue number5
DOIs
StatePublished - May 1 2002
Event17th International Conference on Information Processing in Medical Imaging (IPMI'01) - Davis, CA, United States
Duration: Jun 1 2001Jun 1 2001

Keywords

  • Automatic sulcal extraction
  • Outer cortex
  • Statistical shape model
  • Unit sphere

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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