Active shape models—their training and application
Computer Vision and Image Understanding
Alignment by Maximization of Mutual Information
International Journal of Computer Vision
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume II - Volume II
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The effect of texture representations on AAM performance
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Shape modeling and analysis with entropy-based particle systems
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
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In order to robustly match a statistical model of shape and appearance (e.g. AAM) to an unseen image, it is crucial to employ a robust model fittness measure. Dense sampling of texture over the region covered by the shape of interest makes comparison of model and image in principle robust. However, when merely texture differences are taken into account, problems with low contrast regions, fuzzy features, global intensity variations, and irregularly varying structures occur.In this paper we introduce a novel entropy-optimized texture model (ETM). We map gray values of training images such that pixels represent anatomical structures optimally in terms of information entropy. We match the ETM to unseen images employing Bayes' law.We validate our approach using four training sets (hearts in basal region, hearts in mid region, brain ventricles, and lumbar vertebrae) and conclude that ETMs perform better than AAMs. Not only they reduce the average point-to-contour error, they are better suited to cope with large texture variances due to different scanners and even modalities.