Entropy-Optimized Texture Models

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.