Active shape models—their training and application
Computer Vision and Image Understanding
Parametrization of closed surfaces for 3-D shape description
Computer Vision and Image Understanding
Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines
International Journal of Computer Vision
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Gaussian Random Fields on Sub-Manifolds for Characterizing Brain Surfaces
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Construction of 3D Shape Models of Femoral Articular Cartilage Using Harmonic Maps
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
Analysis of Human Locomotion based on Partial Measurements
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Implicit, view invariant, linear flexible shape modelling
Pattern Recognition Letters - Special issue: Advances in pattern recognition
Shape Registration in Implicit Spaces Using Information Theory and Free Form Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new scheme for automated 3D PDM construction using deformable models
Image and Vision Computing
A minimum description length objective function for groupwise non-rigid image registration
Image and Vision Computing
ISBMS '08 Proceedings of the 4th international symposium on Biomedical Simulation
MRI Bone Segmentation Using Deformable Models and Shape Priors
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Particle-Based Shape Analysis of Multi-object Complexes
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Weakly Supervised Group-Wise Model Learning Based on Discrete Optimization
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Automated segmentation of the menisci from MR images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Shape modeling and analysis with entropy-based particle systems
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Non-parametric surface-based regularisation for building statistical shape models
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Robust autonomous model learning from 2D and 3D data sets
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
ICDHM'07 Proceedings of the 1st international conference on Digital human modeling
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
A method for quantitative evaluation of statistical shape models using morphometry
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Constructing part-based models for groupwise registration
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
Deformable object modelling and matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
3D active shape models using gradient descent optimization of description length
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Capturing anatomical shape variability using b-spline registration
IPMI'05 Proceedings of the 19th international conference on Information Processing in Medical Imaging
Minimum description length shape model based on elliptic fourier descriptors
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
A learning based approach for 3d segmentation and colon detagging
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
Local shape modelling using warplets
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
3D statistical shape models to embed spatial relationship information
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
3D statistical shape modeling of long bones
WBIR'06 Proceedings of the Third international conference on Biomedical Image Registration
A Reparameterisation Based Approach to Geodesic Constrained Solvers for Curve Matching
International Journal of Computer Vision
3D anatomical shape atlas construction using mesh quality preserved deformable models
Computer Vision and Image Understanding
Computer Methods and Programs in Biomedicine
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We describe an automatic method for building optimal 3D statistical shape models from sets of training shapes. Although shape models show considerable promise as a basis for segmenting and interpreting images, a major drawback of the approach is the need to establish a dense correspondence across a training set of example shapes. It is important to establish the correct correspondence, otherwise poor models can result. In 2D, this can be achieved using manual 'landmarks', but in 3D this becomes impractical. We show it is possible to establish correspondences automatically, by casting the correspondence problem as one of finding the 'optimal' parameterisation of each shape in the training set. We describe an explicit representation of surface parameterisation, that ensures the resulting correspondences are legal, and show how this representation can be manipulated to minimise the description length of the training set using the model. This results in compact models with good generalisation properties. Results are reported for two sets of biomedical shapes, showing significant improvement in model properties compared to those obtained using a uniform surface parameterisation.