Statistical Shape Analysis for Brain Structures

Li Shen, Shan Cong, Mark Inlow

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Statistical shape analysis is a fundamental topic in biomedical image computing, and plays important roles in numerous applications in brain imaging. This chapter describes typical shape analysis methods for modeling and analyzing 3D surface data in brain imaging studies. These studies examine various structures of interest in the brain, and aim to identify morphometric differences associated with a particular condition to aid diagnosis and treatment. We first present classic computational methods for modeling and registering 3D surfaces, and then discuss advanced methods that take into consideration subfield information on the surface. After that, we describe techniques for shape analysis of registered surface models, including traditional general linear models as well as a newly proposed statistical learning model for performing surface-based morphometry. Finally, we provide a real world neuroimaging application to demonstrate the effectiveness of these techniques.

Original languageEnglish (US)
Title of host publicationStatistical Shape and Deformation Analysis
Subtitle of host publicationMethods, Implementation and Applications
PublisherElsevier Inc.
Pages351-378
Number of pages28
ISBN (Electronic)9780128104941
ISBN (Print)9780128104934
DOIs
StatePublished - Mar 23 2017

Keywords

  • Distribution analysis
  • Hippocampal subfield
  • Hippocampus
  • Magnetic resonance imaging
  • Random field theory
  • Spherical harmonics
  • Spherical parameterization
  • Statistical parametric mapping
  • Surface registration
  • Surface-based morphometry

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Shen, L., Cong, S., & Inlow, M. (2017). Statistical Shape Analysis for Brain Structures. In Statistical Shape and Deformation Analysis: Methods, Implementation and Applications (pp. 351-378). Elsevier Inc.. https://doi.org/10.1016/B978-0-12-810493-4.00016-X