Statistical shape model for automatic skull-stripping of brain images

Zhiqiang Lao, Dinggang Shen, Christos Davatzikos

Research output: Contribution to journalArticlepeer-review

Abstract

This paper presents a statistical shape model for automatic skull stripping of MR brain images. A surface model of the brain boundary is hierarchically represented by a set of overlapping surface patches, each of which has elastic properties and deformation range that is learned from a training set. The model's deformation is hierarchical which adds robustness to local minima. Moreover, the deformation of the model is constrained and guided by global shape statistics. The model is deformed to the brain boundary by a procedure that matches the local image structures and evaluates the similarity in the whole patch rather than on a single vertex. The experimental results show high agreement between automatic and supervised skull-stripping results.

Original languageEnglish (US)
Article number1029394
Pages (from-to)855-858
Number of pages4
JournalProceedings - International Symposium on Biomedical Imaging
Volume2002-January
DOIs
StatePublished - Jan 1 2002

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Statistical shape model for automatic skull-stripping of brain images'. Together they form a unique fingerprint.

Cite this