Statistical Shape Models (SSMs) are one of the most successful tools in 3Dimage analysis and especially medical image segmentation. By modeling the variability of a population of training shapes, the statistical information inherent in such data are used for automatic interpretation of new images. However, building a high-quality SSM requires manually generated ground truth data from clinical experts. Unfortunately, the acquisition of such data is a time-consuming, error-prone and subjective process. Due to this effort, the majority of SSMs is often based on a limited set of this ground truth training data, which makes the models less statistically meaningful. On the other hand, image data itself is abundant in clinics from daily routine. In this work, methods for automatically constructing a reliable SSM without the need of manual image interpretation from experts are proposed. Thus, the training data is assumed to be the result of any segmentation algorithm or may originate from other sources, e.g. non-expert manual delineations. Depending on the algorithm, the output segmentations will contain errors to a higher or lower degree. In order to account for these errors, areas of low probability of being a boundary should be excluded from the training of the SSM. Therefore, the probabilities are estimated with the help of image-based approaches. By including many shape variations, the corrupted parts can be statistically reconstructed. Two approaches for reconstruction are proposed - an Imputation method and Weighted Robust Principal Component Analysis (WRPCA). This allows the inclusion of many data sets from clinical routine, covering a lot more variations of shape examples. To assess the quality of the models, which are robust against erroneous training shapes, an evaluation compares the generalization and specificity ability to a model build from ground truth data. The results show, that especially WRPCA is a powerful tool to handle corrupted parts and yields to reasonable models, which have a higher quality than the initial segmentations.
Das Kommunikationsverhalten hat sich in den letzten Jahren durch die Smartphonenutzung verändert. Die Nutzer kommunizieren oft nur noch über den elektronischen Weg. Die persönliche Kommunikation, außerhalb des Smartphones, nimmt ab. Das Umfeld gerät unterdessen in Vergessenheit. In der vorliegenden Arbeit werden verschiedene Spielkonzepte entwickelt, welche die Kommunikation steigern sollen. Realisiert wird der Ansatz in einer prototypischen Stadtführer-App, nach den Spielkonzepten von "Scotland Yard" und "Schnitzeljagd". Während der Nutzung müssen die Spieler verschiedene Aufgaben lösen. Welches Spielkonzept sich in Bezug auf die Kommunikationsförderung am besten eignet, wird in einer Evaluation analysiert.
