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
3D Statistical Shape Models Using Direct Optimisation of Description Length
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Error Metrics for Quantitative Evaluation of Medical Image Segmentation
Proceedings of the Theoretical Foundations of Computer Vision, TFCV on Performance Characterization in Computer Vision
Statistical Models of Shape: Optimisation and Evaluation
Statistical Models of Shape: Optimisation and Evaluation
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
Optimal initialization for 3D correspondence optimization: an evaluation study
IPMI'11 Proceedings of the 22nd international conference on Information processing in medical imaging
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We introduce a novel method for the evaluation of statistical shape models (SSM) that allows for quantifying the model quality wrt. global and local shape properties. The construction of SSM requires the identification of corresponding landmarks across a set of training shapes. Establishing such correspondence is a delicate matter and demands for automatic methods in a 3D setting. Conversely, the model quality needs to be evaluated to be able to compare different SSM in terms of specificity and generalization ability and to further improve the process of establishing correspondence. These well-known quantitative evaluation measures can be analyzed using various distance functions. The problem with popular landmark based metrics however is that the shape similarity of both the generated SSM and the actual object is disregarded. Evaluation of various models reveals that this can significantly corrupt the quality measures of the respective SSM, whereas the proposed method provides feasible results.