Results 211 to 220 of about 53,852 (253)

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
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Statistical shape and appearance models of bones

Bone, 2014
When applied to bones, statistical shape models (SSM) and statistical appearance models (SAM) respectively describe the mean shape and mean density distribution of bones within a certain population as well as the main modes of variations of shape and density distribution from their mean values.
Nazli, Sarkalkan, Harrie, Weinans, Amir A, Zadpoor   +2 more
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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
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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
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Multi-resolution Statistical Shape Models for Multi-organ Shape Modelling

2020
Statistical shape models (SSMs) are widely used in medical image segmentation. However, traditional SSM methods suffer from the High-Dimension-Low-Sample-Size (HDLSS) problem in modelling. In this work, we extend the state-of-the-art multi-resolution SSM approach from two dimension (2D) to three dimension (3D) and from single organ to multiple organs ...
Zhonghua Chen, Tapani Ristaniemi, Fengyu Cong, Hongkai Wang 0002   +3 more
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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
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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
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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
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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
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Independent component analysis in statistical shape models

SPIE Proceedings, 2003
Statistical shape models generally use Principal Component Analysis (PCA) to describe the main directions of shape variation in a training set of example shapes. However, PCA assumes a number of restrictions on the data that do not always hold. In this paper we explore the use of an alternative shape decomposition, Independent Component Analysis (ICA),
Mehmet Üzümcü, Alejandro F. Frangi, Johan H. C. Reiber, Boudewijn P. F. Lelieveldt   +3 more
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