Statistical methods in computational anatomy

@article{Miller1997StatisticalMI,
  title={Statistical methods in computational anatomy},
  author={M. I. Miller and A Banerjee and Gary E. Christensen and Sarang C. Joshi and Navin Khaneja and Ulf Grenander and Larisa Matejic},
  journal={Statistical Methods in Medical Research},
  year={1997},
  volume={6},
  pages={267 - 299}
}
This paper reviews recent developments by the Washington/Brown groups for the study of anatomical shape in the emerging new discipline of computational anatomy. Parametric representations of anatomical variation for computational anatomy are reviewed, restricted to the assumption of small deformations. The generation of covariance operators for probabilistic measures of anatomical variation on coordinatized submanifolds is formulated as an empirical procedure. Populations of brains are mapped… 
A Stochastic Large Deformation Model for Computational Anatomy
TLDR
A stochastic model for incorporating random variations into the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework is introduced and the template estimation problem for landmarks with noise is formulated and two methods for efficiently estimating the parameters of the noise fields from a prescribed data set are given.
Computational anatomy and neuropsychiatric disease: probabilistic assessment of variation and statistical inference of group difference, hemispheric asymmetry, and time-dependent change
Three components of computational anatomy (CA) are reviewed in this paper: (i) the computation of large-deformation maps, that is, for any given coordinate system representations of two anatomies,
Computational anatomy: an emerging discipline
This paper studies mathematical methods in the emerging new discipline of Computational Anatomy. Herein we formalize the Brown/Washington University model of anatomy following the global pattern
Multivariate models of inter-subject anatomical variability
This paper presents a very selective review of some of the approaches for multivariate modelling of inter-subject variability among brain images. It focusses on applying probabilistic kernel-based
Medical image analysis via frechet means of diffeomorphisms
The construction of average models of anatomy, as well as regression analysis of anatomical structures, are key issues in medical research, e.g., in the study of brain development and disease
A probabilistic ribbon model for shape analysis of the cerebral sulci: application to the central sulcus.
TLDR
The use of this shape representation in cortical morphometric analysis applications is demonstrated, in particular in obtaining local depth and curvature measurements of a sulcus as well as in determining average shapes and variability.
On the metrics and euler-lagrange equations of computational anatomy.
TLDR
Current experimental results from the Toga & Thompson group in growth, the Van Essen group in macaque and human cortex mapping, and the Csernansky group in hippocampus mapping for neuropsychiatric studies in aging and schizophrenia are shown.
Parameterization of 3D brain structures for statistical shape analysis
TLDR
This paper incorporates shape-based landmarks into parameterization of banana-like 3D brain structures and a multi-resolution shape representation is obtained by using the Discrete Fourier Transform.
Groupwise morphometric analysis based on morphological appearance manifold
  • Naixiang Lian, C. Davatzikos
  • Mathematics, Computer Science
    2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
  • 2009
TLDR
This work estimates the morphological appearance manifold obtained by varying parameters of the template warping procedure, which is then used for groupwise registration and statistical analysis of biomedical images, by employing a minimum variance criterion on selected complete morphological descriptor to perform manifold-constrained optimization.
Geometric statistics for computational anatomy
This thesis develops Geometric Statistics to analyze the normal and pathological variability of organ shapes in Computational Anatomy. Geometric statistics consider data that belong to manifolds
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