Model-order selection in statistical shape models
This work addresses a specific issue in medical imaging or shape analysis for researchers using statistical shape models, but it appears incremental as it builds on existing model-order selection methods.
The paper tackles the problem of selecting the model order in Point Distribution Models for statistical shape models, which is crucial for balancing over- and underfitting. It presents an information-theoretic technique that empirically provides a good trade-off, though no specific numerical results are mentioned.
Statistical shape models enhance machine learning algorithms providing prior information about deformation. A Point Distribution Model (PDM) is a popular landmark-based statistical shape model for segmentation. It requires choosing a model order, which determines how much of the variation seen in the training data is accounted for by the PDM. A good choice of the model order depends on the number of training samples and the noise level in the training data set. Yet the most common approach for choosing the model order simply keeps a predetermined percentage of the total shape variation. In this paper, we present a technique for choosing the model order based on information-theoretic criteria, and we show empirical evidence that the model order chosen by this technique provides a good trade-off between over- and underfitting.