<unk> <unk>  is a specific suite of algorithms used for<unk><unk>  and manipulating dense imagery based on<unk><unk><unk>  within the academic discipline of computational anatomy, to be distinguished from its precursor based on<unk><unk> . The distinction between the two is that<unk><unk>  maps satisfy the property that the length associated to their flow away from the identity induces a<unk>  on the group of<unk> ms, which in turn induces a<unk>  on the orbit of shapes and forms within the field of Computational Anatomy. The study of shapes and forms with the<unk>  of<unk><unk><unk>  is called diffeomorphometry. A<unk><unk>  system is a system designed to map, manipulate, and transfer information which is stored in many types of spatially distributed medical imagery.<unk><unk>  is the underlying technology for<unk>  and analyzing information measured in human anatomical coordinate systems which have been measured via Medical imaging.<unk><unk>  is a broad term that actually refers to a number of different algorithms, processes, and methods. It is attached to many operations and has many applications for analysis and visualization.<unk><unk>  can be used to relate various sources of information which are indexed as a function of spatial position as the key index variable.<unk> ms are by their Latin root structure preserving transformations, which are in turn differentiable and therefore smooth, allowing for the calculation of<unk>  based quantities such as arc length and surface areas. Spatial location and extents in human anatomical coordinate systems can be recorded via a variety of Medical imaging modalities, generally termed multi-modal medical imagery, providing either scalar and or vector quantities at each spatial location. Examples are scalar T1 or T2 magnetic resonance imagery, or as 3x3 diffusion tensor<unk> es diffusion MRI and diffusion-weighted imaging, to scalar densities associated to computed tomography (CT), or functional imagery such as temporal data of functional magnetic resonance imaging and scalar densities such as Positron emission tomography (PET). Computational anatomy is a subdiscipline within the broader field of neuroinformatics within bioinformatics and medical imaging. The first algorithm for dense image<unk>  via<unk><unk><unk>  was Beg's<unk>  for volumes and Joshi's landmark matching for point sets with correspondence, with<unk>  algorithms now available for computing<unk><unk>  maps between non-corresponding landmarks and landmark matching intrinsic to spherical manifolds, curves, currents and surfaces, tensors, varifolds, and time-series. The term<unk>  was first established as part of the National Institutes of Health supported Biomedical Informatics Research Network. In a more general sense,<unk><unk>  is any solution that registers or builds correspondences between dense coordinate systems in medical imaging by ensuring the solutions are<unk> . There are now many codes organized around<unk>  registration including ANTS, DARTEL, DEMONS, StationaryLDDMM, FastLDDMM, as examples of actively used computational codes for constructing correspondences between coordinate systems based on dense images. The distinction between<unk><unk><unk>  forming the basis for<unk>  and the earliest methods of<unk><unk>  is the introduction of a Hamilton principle of least-action in which<unk><unk> s are selected of shortest length corresponding to geodesic flows. This important distinction arises from the original formulation of the Riemannian<unk>  corresponding to the right-invariance. The lengths of these geodesics give the<unk>  in the<unk>  space structure of human anatomy. Non-geodesic formulations of<unk><unk>  in general does not correspond to any<unk>  formulation.
Large deformation diffeomorphic metric mapping
Swedish interactive thresholding algorithm
Sham peer review
CAN approach
Triage
Reed–Frost model
Consensus error grid
EFAR System Model