scholarly journals Use of a CT statistical deformation model for multi-modal pelvic bone segmentation

2008 ◽  
Author(s):  
Stephen Thompson ◽  
Graeme Penney ◽  
Damien Buie ◽  
Prokar Dasgupta ◽  
Dave Hawkes
Keyword(s):  
Pelvic Bone ◽  
Deformation Model ◽  
Bone Segmentation ◽  
Statistical Deformation Model

Non-rigid brain image registration using a statistical deformation model

2006 ◽  
Author(s):  
Jeroen Wouters ◽  
Emiliano D'Agostino ◽  
Frederik Maes ◽  
Dirk Vandermeulen ◽  
Paul Suetens
Keyword(s):  
Image Registration ◽  
Deformation Model ◽  
Brain Image ◽  
Statistical Deformation Model

scholarly journals Sparse Statistical Deformation Model for the Analysis of Craniofacial Malformations in the Crouzon Mouse

2007 ◽  
pp. 112-121 ◽  
Author(s):  
Hildur Ólafsdóttir ◽  
Michael Sass Hansen ◽  
Karl Sjöstrand ◽  
Tron A. Darvann ◽  
Nuno V. Hermann ◽  
...  
Keyword(s):  
Deformation Model ◽  
Craniofacial Malformations ◽  
Statistical Deformation Model

scholarly journals Finite Element Surface Registration Incorporating Curvature, Volume Preservation, and Statistical Model Information

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Thomas Albrecht ◽  
Andreas Dedner ◽  
Marcel Lüthi ◽  
Thomas Vetter
Keyword(s):  
Finite Element ◽  
Adaptive Mesh Refinement ◽  
Adaptive Mesh ◽  
Surface Registration ◽  
Deformation Model ◽  
Registration Method ◽  
Element Discretization ◽  
Volume Preservation ◽  
Continuous Formulation ◽  
Statistical Deformation Model

We present a novel method for nonrigid registration of 3D surfaces and images. The method can be used to register surfaces by means of their distance images, or to register medical images directly. It is formulated as a minimization problem of a sum of several terms representing the desired properties of a registration result: smoothness, volume preservation, matching of the surface, its curvature, and possible other feature images, as well as consistency with previous registration results of similar objects, represented by a statistical deformation model. While most of these concepts are already known, we present a coherent continuous formulation of these constraints, including the statistical deformation model. This continuous formulation renders the registration method independent of its discretization. The finite element discretization we present is, while independent of the registration functional, the second main contribution of this paper. The local discontinuous Galerkin method has not previously been used in image registration, and it provides an efficient and general framework to discretize each of the terms of our functional. Computational efficiency and modest memory consumption are achieved thanks to parallelization and locally adaptive mesh refinement. This allows for the first time the use of otherwise prohibitively large 3D statistical deformation models.

Pelvic Bone Segmentation on T1-Weighted MR Images

2011 ◽  
Vol 81 (2) ◽  
pp. S819
Author(s):  
G. Novak ◽  
G. Nyiri ◽  
K. Hwang ◽  
L. Dong ◽  
M. Fidrich
Keyword(s):  
Pelvic Bone ◽  
Bone Segmentation ◽  
Mr Images
Sign in / Sign up

Export Citation Format

Share Document