75,576 research outputs found

    Automatic Multi-Model Fitting for Blood Vessel Extraction

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    Blood vessel extraction and visualization in 2D images or 3D volumes is an essential clinical task. A blood vessel system is an example of a tubular tree like structure, and fully automated reconstruction of tubular tree like structures remains an open computer vision problem. Most vessel extraction methods are based on the vesselness measure. A vesselness measure, usually based on the eigenvalues of the Hessian matrix, assigns a high value to a voxel that is likely to be a part of a blood vessel. After the vesselness measure is computed, most methods extract vessels based on the shortest paths connecting voxels with a high measure of vesselness. Our approach is quite different. We also start with the vesselness measure, but instead of computing shortest paths, we propose to fit a geometric of vessel system to the vesselness measure. Fitting a geometric model has the advantage that we can choose a model with desired properties and the appropriate goodness-of-fit function to control the fitting results. Changing the model and goodness-of-fit function allows us to change the properties of the reconstructed vessel system structure in a principled way. In contrast, with shortest paths, any undesirable reconstruction properties, such as short-cutting, is addressed by developing ad-hock procedures that are not easy to control. Since the geometric model has to be fitted to a discrete set of points, we threshold the vesselness measure to extract voxels that are likely to be vessels, and fit our geometric model to these thresholded voxels. Our geometric model is a piecewise-line segment model. That is we approximate the vessel structure as a collection of 3D straight line segments of various lengths and widths. This can be regarded as the problem of fitting multiple line segments, that is a multi-model fitting problem. We approach the multi-model fitting problem in the global energy optimization framework. That is we formulate a global energy function that reflects the goodness of fit of our piecewise line segment model to the thresholded vesselness voxels and we use the efficient and effective graph cut algorithm to optimize the energy. Our global energy function consists of the data, smoothness and label cost. The data cost encourages a good geometric fit of each voxel to the line segment it is being assigned to. The smoothness cost encourages nearby line segments to have similar angles, thus encouraging smoother blood vessels. The label cost penalizes overly complex models, that is, it encourages to explain the data with fewer line segment models. We apply our algorithm to the challenging 3D data that are micro-CT images of a mouse heart and obtain promising results

    3D reconstruction of ribcage geometry from biplanar radiographs using a statistical parametric model approach

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    Rib cage 3D reconstruction is an important prerequisite for thoracic spine modelling, particularly for studies of the deformed thorax in adolescent idiopathic scoliosis. This study proposes a new method for rib cage 3D reconstruction from biplanar radiographs, using a statistical parametric model approach. Simplified parametric models were defined at the hierarchical levels of rib cage surface, rib midline and rib surface, and applied on a database of 86 trunks. The resulting parameter database served to statistical models learning which were used to quickly provide a first estimate of the reconstruction from identifications on both radiographs. This solution was then refined by manual adjustments in order to improve the matching between model and image. Accuracy was assessed by comparison with 29 rib cages from CT scans in terms of geometrical parameter differences and in terms of line-to-line error distance between the rib midlines. Intra and inter-observer reproducibility were determined regarding 20 scoliotic patients. The first estimate (mean reconstruction time of 2’30) was sufficient to extract the main rib cage global parameters with a 95% confidence interval lower than 7%, 8%, 2% and 4° for rib cage volume, antero-posterior and lateral maximal diameters and maximal rib hump, respectively. The mean error distance was 5.4 mm (max 35mm) down to 3.6 mm (max 24 mm) after the manual adjustment step (+3’30). The proposed method will improve developments of rib cage finite element modeling and evaluation of clinical outcomes.This work was funded by Paris Tech BiomecAM chair on subject specific muscular skeletal modeling, and we express our acknowledgments to the chair founders: Cotrel foundation, Société générale, Protéor Company and COVEA consortium. We extend your acknowledgements to Alina Badina for medical imaging data, Alexandre Journé for his advices, and Thomas Joubert for his technical support

    Data-Driven Shape Analysis and Processing

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    Data-driven methods play an increasingly important role in discovering geometric, structural, and semantic relationships between 3D shapes in collections, and applying this analysis to support intelligent modeling, editing, and visualization of geometric data. In contrast to traditional approaches, a key feature of data-driven approaches is that they aggregate information from a collection of shapes to improve the analysis and processing of individual shapes. In addition, they are able to learn models that reason about properties and relationships of shapes without relying on hard-coded rules or explicitly programmed instructions. We provide an overview of the main concepts and components of these techniques, and discuss their application to shape classification, segmentation, matching, reconstruction, modeling and exploration, as well as scene analysis and synthesis, through reviewing the literature and relating the existing works with both qualitative and numerical comparisons. We conclude our report with ideas that can inspire future research in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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