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Titlebook: A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis; Heike Hufnagel Book 2011 Vieweg+Teubner Verlag | Sprin

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期刊全称A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis
影响因子2023Heike Hufnagel
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学科分类Aktuelle Forschung Medizintechnik – Latest Research in Medical Engineering
图书封面Titlebook: A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis;  Heike Hufnagel Book 2011 Vieweg+Teubner Verlag | Sprin
影响因子In medical image analysis, major areas such as radiotherapy, surgery planning, and quantitative diagnostics benefit from shape modeling to facilitate solutions to analysis, segmentation and reconstruction problems. .Heike Hufnagel proposes a mathematically sound statistical shape model using correspondence probabilities instead of 1-to-1 correspondences. The explicit probabilistic model is employed as shape prior in an implicit level set segmentation. Due to the particular attributes of the new model, the challenging integration of explicit and implicit representations can be done in an elegant mathematical formulation, thus combining the advantages of both explicit model and implicit segmentation. Evaluations are performed to depict the characteristics and strengths of the new model and segmentation method...The dissertation has received the Fokusfinder award 2011 by the Innovationsstiftung Schleswig-Holstein (ISH), the Basler AG and Philips Medical Systems...
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978-3-8348-1722-8Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden GmbH, Wiesbaden 2011
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A Probabilistic Framework for Point-Based Shape Modeling in Medical Image Analysis978-3-8348-8600-2Series ISSN 2625-9354 Series E-ISSN 2625-9362
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https://doi.org/10.1007/978-3-642-99970-3 in the literature and to gather knowledge about its behaviour and characteristics for different types of shapes. In section 4.1, the performance measures which are commonly used to assess the quality of SSMs are presented and discussed, and several distance metrics that are suited for point-based S
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Differential and Difference Inequalities,ue inhomogeneities, contour gaps, and noise the automatic segmentation of many anatomical structures remains a challenge. Low-level algorithms as region growing, thresholding or simple edge-detection are often bound to fail or require heavy user interaction to lead to acceptable segmentation results
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Differential and Difference Inequalities,the literature. The emphasis of this thesis however was not so much to propose a convenient SSM to solve a specific practical problem but to investigate the possibilities of a novel approach to SSM computation. The focus of this manuscript is twofold: First, a novel SSM method was developed in a pro
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