@inproceedings{DickmannKasparLoehnhardtetal.2009, author = {Dickmann, Frank and Kaspar, Mathias and L{\"o}hnhardt, Benjamin and Kepper, Nick and Viezens, Fred and Hertel, Frank and Lesnussa, Michael and Mohammed, Yassene and Thiel, Andreas and Steinke, Thomas and Bernarding, Johannes and Krefting, Dagmar and Knoch, Tobias and Sax, Ulrich}, title = {Visualization in Health Grid Environments: A Novel Service and Business Approach}, series = {GECON}, volume = {5745}, booktitle = {GECON}, publisher = {Springer}, doi = {10.1007/978-3-642-03864-8_12}, pages = {150 -- 159}, year = {2009}, language = {en} } @inproceedings{LuetzkendorfViezensHerteletal.2009, author = {L{\"u}tzkendorf, Ralf and Viezens, Fred and Hertel, Frank and Krefting, Dagmar and Peter, Kathrin and Bernarding, Johannes}, title = {Performanzsteigerung von Diffusion Tensor Image Analyse durch Nutzung gridbasierter Workflows}, series = {GMDS}, booktitle = {GMDS}, doi = {10.3205/09gmds196}, year = {2009}, language = {en} } @inproceedings{HertelKreftingLuetzkendorfetal.2009, author = {Hertel, Frank and Krefting, Dagmar and L{\"u}tzkendorf, Ralf and Viezens, Fred and Thiel, Andreas and Peter, Kathrin and Bernarding, Johannes}, title = {Diffusions-Tensor-Imaging als Gridanwendung - Perfomanzsteigerung und standortunabh{\"a}ngiger Zugang zu leistungsf{\"a}higen Ressourcen}, series = {GI Jahrestagung'09}, booktitle = {GI Jahrestagung'09}, pages = {1233 -- 1240}, year = {2009}, language = {en} } @article{BernardSalamancaThunbergetal., author = {Bernard, Florian and Salamanca, Luis and Thunberg, Johan and Tack, Alexander and Jentsch, Dennis and Lamecker, Hans and Zachow, Stefan and Hertel, Frank and Goncalves, Jorge and Gemmar, Peter}, title = {Shape-aware Surface Reconstruction from Sparse Data}, series = {arXiv}, journal = {arXiv}, pages = {1602.08425v1}, abstract = {The reconstruction of an object's shape or surface from a set of 3D points is a common topic in materials and life sciences, computationally handled in computer graphics. Such points usually stem from optical or tactile 3D coordinate measuring equipment. Surface reconstruction also appears in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or the alignment of intra-operative navigation and preoperative planning data. In contrast to mere 3D point clouds, medical imaging yields contextual information on the 3D point data that can be used to adopt prior information on the shape that is to be reconstructed from the measurements. In this work we propose to use a statistical shape model (SSM) as a prior for surface reconstruction. The prior knowledge is represented by a point distribution model (PDM) that is associated with a surface mesh. Using the shape distribution that is modelled by the PDM, we reformulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given measurements are interpreted as samples of the GMM. By using mixture components with anisotropic covariances that are oriented according to the surface normals at the PDM points, a surface-based tting is accomplished. By estimating the parameters of the GMM in a maximum a posteriori manner, the reconstruction of the surface from the given measurements is achieved. Extensive experiments suggest that our proposed approach leads to superior surface reconstructions compared to Iterative Closest Point (ICP) methods.}, language = {en} } @article{BernardSalamancaThunbergetal., author = {Bernard, Florian and Salamanca, Luis and Thunberg, Johan and Tack, Alexander and Jentsch, Dennis and Lamecker, Hans and Zachow, Stefan and Hertel, Frank and Goncalves, Jorge and Gemmar, Peter}, title = {Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds}, series = {Medical Image Analysis}, volume = {38}, journal = {Medical Image Analysis}, doi = {10.1016/j.media.2017.02.005}, pages = {77 -- 89}, abstract = {The reconstruction of an object's shape or surface from a set of 3D points plays an important role in medical image analysis, e.g. in anatomy reconstruction from tomographic measurements or in the process of aligning intra-operative navigation and preoperative planning data. In such scenarios, one usually has to deal with sparse data, which significantly aggravates the problem of reconstruction. However, medical applications often provide contextual information about the 3D point data that allow to incorporate prior knowledge about the shape that is to be reconstructed. To this end, we propose the use of a statistical shape model (SSM) as a prior for surface reconstruction. The SSM is represented by a point distribution model (PDM), which is associated with a surface mesh. Using the shape distribution that is modelled by the PDM, we formulate the problem of surface reconstruction from a probabilistic perspective based on a Gaussian Mixture Model (GMM). In order to do so, the given points are interpreted as samples of the GMM. By using mixture components with anisotropic covariances that are "oriented" according to the surface normals at the PDM points, a surface-based fitting is accomplished. Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points. We compare our method to the extensively used Iterative Closest Points method on several different anatomical datasets/SSMs (brain, femur, tibia, hip, liver) and demonstrate superior accuracy and robustness on sparse data.}, language = {en} }