TY - CHAP A1 - Dickmann, Frank A1 - Kaspar, Mathias A1 - Löhnhardt, Benjamin A1 - Kepper, Nick A1 - Viezens, Fred A1 - Hertel, Frank A1 - Lesnussa, Michael A1 - Mohammed, Yassene A1 - Thiel, Andreas A1 - Steinke, Thomas A1 - Bernarding, Johannes A1 - Krefting, Dagmar A1 - Knoch, Tobias A1 - Sax, Ulrich T1 - Visualization in Health Grid Environments: A Novel Service and Business Approach T2 - GECON Y1 - 2009 U6 - https://doi.org/10.1007/978-3-642-03864-8_12 VL - 5745 SP - 150 EP - 159 PB - Springer ER - TY - CHAP A1 - Lützkendorf, Ralf A1 - Viezens, Fred A1 - Hertel, Frank A1 - Krefting, Dagmar A1 - Peter, Kathrin A1 - Bernarding, Johannes T1 - Performanzsteigerung von Diffusion Tensor Image Analyse durch Nutzung gridbasierter Workflows T2 - GMDS Y1 - 2009 U6 - https://doi.org/10.3205/09gmds196 ER - TY - CHAP A1 - Hertel, Frank A1 - Krefting, Dagmar A1 - Lützkendorf, Ralf A1 - Viezens, Fred A1 - Thiel, Andreas A1 - Peter, Kathrin A1 - Bernarding, Johannes T1 - Diffusions-Tensor-Imaging als Gridanwendung - Perfomanzsteigerung und standortunabhängiger Zugang zu leistungsfähigen Ressourcen T2 - GI Jahrestagung’09 Y1 - 2009 SP - 1233 EP - 1240 ER - TY - JOUR A1 - Bernard, Florian A1 - Salamanca, Luis A1 - Thunberg, Johan A1 - Tack, Alexander A1 - Jentsch, Dennis A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hertel, Frank A1 - Goncalves, Jorge A1 - Gemmar, Peter T1 - Shape-aware Surface Reconstruction from Sparse Data JF - arXiv N2 - 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. Y1 - 2016 SP - 1602.08425v1 ER - TY - JOUR A1 - Bernard, Florian A1 - Salamanca, Luis A1 - Thunberg, Johan A1 - Tack, Alexander A1 - Jentsch, Dennis A1 - Lamecker, Hans A1 - Zachow, Stefan A1 - Hertel, Frank A1 - Goncalves, Jorge A1 - Gemmar, Peter T1 - Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds JF - Medical Image Analysis N2 - 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. Y1 - 2017 UR - http://www.sciencedirect.com/science/article/pii/S1361841517300233 U6 - https://doi.org/10.1016/j.media.2017.02.005 VL - 38 SP - 77 EP - 89 ER -