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Statistical Atlases

  • C. DavatzikosEmail author
  • R. Verma
  • D. Shen

Abstract

This chapter discusses the general concept of statistical atlases built from medical images. A statistical atlas is a quantitative reflection of normal variability in anatomy, function, pathology, or other imaging measurements, and it allows us to establish a baseline against which abnormal images are to be compared for diagnostic or treatment planning purposes. Constructing a statistical atlas relies on a fundamental building block, namely deformable registration, which maps imaging data from many individuals to a common coordinate system, so that statistics of normal variability, as well as abnormal deviations from it, can be performed. 3D and 4D registration methods are discussed. This chapter also discusses the statistical analyses applied to co-registered normative images, and finally briefly touches upon use of machine learning for detection of imaging patterns that distinctly deviate from the normative range to allow for individualized classification.

Keywords

Attribute Vector Diffusion Tensor Image Data Manifold Learning Deformable Image Registration Shape Transformation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of RadiologyUniversity of PennsylvaniaPhiladelphiaUSA
  3. 3.Department of RadiologyUniversity of North CarolinaChapel HillUSA

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