Results 241 to 250 of about 440,016 (278)

Measurement error in statistical models of shape

Computer Methods and Programs in Biomedicine, 2011
Active shape models (ASMs) are popular and sophisticated methods of extracting features in (especially medical) images. Here we analyse the error in placing ASM points on the boundary of the feature. By using replications, a corrected covariance matrix is presented that should reduce the effects of placement error.
Damian J. J. Farnell, Andrew Pickles, Christopher Roberts   +2 more
openaire   +3 more sources

Reparameterising 3D Statistical Shape Models

2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), 2019
3D statistical shape models are widely used in modelling 3D shapes such as human faces and bodies. The limitation of such model is that, once built, the model can only represent 3D shape instances of a fixed mesh topology. While some applications may require a shape model of a different mesh topology, the model building pipeline has to be repeated with
Haoyang Wang 0002, Stefanos Zafeiriou
openaire   +1 more source

Building a statistical shape model of the pelvis

International Congress Series, 2004
Abstract Statistical shape models of anatomical structures such as bones can simplify and improve 3D segmentation and the registration to sparse and noisy input data. In this paper, a novel surface-based technique to generate statistical shape models from segmented CT data sets is presented.
Sebastian Meller, Willi A. Kalender
openaire   +1 more source

A Locally Deformable Statistical Shape Model

2011
Statistical shape models are one of the most powerful methods in medical image segmentation problems. However, if the task is to segment complex structures, they are often too constrained to capture the full amount of anatomical variation. This is due to the fact that the number of training samples is limited in general, because generating hand ...
Carsten Last, Simon Winkelbach, Friedrich M. Wahl, Klaus W. G. Eichhorn, Friedrich Bootz   +4 more
openaire   +1 more source

Design of a statistical model of brain shape

1997
This paper describes a statistical shape model of the brain extending through the whole organ. The variability in a normal population is described by global deformation modes. The model is based on the analysis of homologous deformations mapping similar structures in brain images.
Lionel Le Briquer, James C. Gee
openaire   +1 more source

An Information Theoretic Approach to Statistical Shape Modelling

Procedings of the British Machine Vision Conference 2001, 2001
Statistical shape models have been used widely as a basis for segmenting and interpreting images. A major drawback of the approach is the need to establish a set of dense correspondences across a training set of segmented shapes. By posing the problem as one of minimising the description length of the model, we develop an efficient method that ...
Davies, R, Cootes, TF; id_orcid 0000-0002-2695-9063, Twining, CJ, Taylor, CJ.   +3 more
openaire   +2 more sources

MULTI-OBJECT STATISTICAL POSE+SHAPE MODELS

2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007
Region of interest (ROI) analysis is a very common procedure for morphometry studies of brain structures, where each structure is usually isolated from the rest of the brain and aligned to a reference shape. In the alignment process all pose information is disregarded. However, considering the brain as a multi-object system formed by several structures,
Matías N. Bossa, Salvador Olmos
openaire   +2 more sources

Consistent Spherical Parameterisation for Statistical Shape Modelling

3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006., 2006
We have described previously a method of automatically constructing statistical models of shape. The method treats model-building as an optimisation problem by re-parameterising each shape so as to minimise the description length of the training set. The approach requires an explicit parameterisation of each shape, which is straightforward in 2D, but ...
Davies, Rhodri H., Twining, Carole J., Taylor, Chris J.; id_orcid 0000-0001-7867-9533   +2 more
openaire   +2 more sources

A Statistical Model for Smooth Shapes in Kendall Shape Space

2015
This paper proposes a novel framework for learning a statistical shape model from image data, automatically without manual annotations. The framework proposes a generative model for image data of individuals within a group, relying on a model of group shape variability.
Akshay V. Gaikwad, Saurabh J. Shigwan, Suyash P. Awate   +2 more
openaire   +1 more source

Symmetry-Factored Statistical Modelling of Craniofacial Shape

2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017
We present a new method for symmetry-factored statistical modelling of 3D shape. Our method comprises three novel components. First, a means to symmetrise a 3D mesh, regularised using the Laplace-Beltrami operator. Second, a symmetry-aware variant of Generalized Procrustes Analysis (GPA).
Hang Dai, William A. P. Smith, Nick E. Pears, Christian Duncan   +3 more
openaire   +1 more source