Decision estimation and classification: an introduction to pattern recognition and related topics
Decision estimation and classification: an introduction to pattern recognition and related topics
Learning flexible models from image sequences
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
Active shape models—their training and application
Computer Vision and Image Understanding
Automatic landmark generation for Point Distribution Models
BMVC 94 Proceedings of the conference on British machine vision (vol. 2)
A Framework for Automatic Landmark Identification Using a New Method of Nonrigid Correspondence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Softassign Procrustes Matching Algorithm
IPMI '97 Proceedings of the 15th International Conference on Information Processing in Medical Imaging
Quasi-Conformally Flat Mapping the Human Cerebellum
MICCAI '99 Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention
An Efficient Method for Constructing Optimal Statistical Shape Models
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Performance Issues in Shape Classification
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part I
A Statistical Shape Model for the Liver
MICCAI '02 Proceedings of the 5th International Conference on Medical Image Computing and Computer-Assisted Intervention-Part II
Strings: Variational Deformable Models of Multivariate Continuous Boundary Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Projected Generalized Procrustes Alignment
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
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Statistical shape models show considerable promise as a basis for segmenting and interpreting images. One of the drawbacks of the approach is, however, the need to establish a set of dense correspondences between examples of similar structures, across a training set of images. Often this is achieved by locating a set of 'landmarks' manually on each of the training images, which is time-consuming and subjective for 2D images, and almost impossible for 3D images. This has led to considerable interest in the problem of building a model automatically from a set of training shapes. We extend previous work that has posed this problem as one of optimising a measure of model 'quality' with respect to the set of correspondences. We define model 'quality' in terms of the information required to code the whole set of training shapes and aim to minimise this description length. We describe a scheme for representing the dense correspondence maps between the training examples and show that a minimum description length model can be obtained by stochastic optimisation. Results are given for several different training sets of 2D boundaries, showing that the automatic method constructs better models than the manual landmarking approach. We also show that the method can be extended straightforwardly to 3D.