Robust automated constellation-based landmark detection in human brain imaging.

ABSTRACT: A robust fully automated algorithm for identifying an arbitrary number of landmark points in the human brain is described and validated. The proposed method combines statistical shape models with trained brain morphometric measures to estimate midbrain landmark positions reliably and accurately. Gross morphometric constraints provided by automatically identified eye centers and the center of the head mass are shown to provide robust initialization in the presence of large rotations in the initial head orientation. Detection of primary midbrain landmarks are used as the foundation from which extended detection of an arbitrary set of secondary landmarks in different brain regions by applying a linear model estimation and principle component analysis. This estimation model sequentially uses t

SUBMITTER: Ghayoor A 

PROVIDER: S-EPMC5630513 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature