9 May 2002 Three-dimensional knowledge-based surface model for segmentation of organic structures
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A 3D surface model for the segmentation of organic structures in CT-and MR-datasets has been developed. The training dataset for the surface model is computed from semiautomatically generated voxelsets. Triangulated meshes of the voxelsets representing the objects' surfaces are generated. The surface model is able to learn the shape variations in the training dataset by a principal component analysis of the information provided by the points forming the triangulated surface-meshes. Furthermore, the image information at the mesh points is also added into gray value models describing the gray value distribution at this particular surface section. The optimization of the model is performed by iteratively moving the surface points of the model towards image structures fitting to the gray value models. During the optimization process the models shape information assures that the surface stays plausible. A 3D-model of the spleen consisting of 10 objects, and a kidney model generated from 7 left kidneys have been developed. The models have been tested on 3 unknown spleen- and 3 unknown kidney-datasets. The total cover between the model and the organs varied between 65% and 75%, which is a respectable result in the face of the small training datasets the models were generated from.
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Michael Kohnen, Andreas H. Mahnken, Joerg Kesten, Eckart Koeppel, Rolf W. Guenther, Berthold B. Wein, "Three-dimensional knowledge-based surface model for segmentation of organic structures", Proc. SPIE 4684, Medical Imaging 2002: Image Processing, (9 May 2002); doi: 10.1117/12.467191; https://doi.org/10.1117/12.467191

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