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.
|Title of host publication||Handbook of Biomedical Imaging: Methodologies and Clinical Research|
|Number of pages||21|
|ISBN (Print)||9780387097497, 9780387097480|
|Publication status||Published - 2015 Jan 1|
ASJC Scopus subject areas
- Computer Science(all)
- Biochemistry, Genetics and Molecular Biology(all)