Interactive Mesh Smoothing for Medical Applications

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dc.contributor.authorMönch, Tobiasen_US
dc.contributor.authorLawonn, Kaien_US
dc.contributor.authorKubisch, Christophen_US
dc.contributor.authorWestermann, Rüdigeren_US
dc.contributor.authorPreim, Bernharden_US
dc.contributor.editorHolly Rushmeier and Oliver Deussenen_US
dc.date.accessioned2015-02-28T16:16:23Z
dc.date.available2015-02-28T16:16:23Z
dc.date.issued2013en_US
dc.description.abstractSurface models derived from medical image data often exhibit artefacts, such as noise and staircases, which can be reduced by applying mesh smoothing filters. Usually, an iterative adaption of smoothing parameters to the specific data and continuous re‐evaluation of accuracy and curvature is required. Depending on the number of vertices and the filter algorithm, computation time may vary strongly and interfere with an interactive mesh generation procedure. In this paper, we present an approach to improve the handling of mesh smoothing filters. Based on a GPU mesh smoothing implementation of uniform and anisotropic filters, model quality is evaluated in real‐time and provided to the user to support the mental optimization of input parameters. This is achieved by means of quality graphs and quality bars. Moreover, this framework is used to find appropriate smoothing parameters automatically and to provide data‐specific parameter suggestions. These suggestions are employed to generate a preview gallery with different smoothing suggestions. The preview functionality is additionally used for the inspection of specific artefacts and their possible reduction with different parameter sets.Surface models derived from medical image data often exhibit artifacts, such as noise and staircases, which can be reduced by applying mesh smoothing filters. Usually, an iterative adaption of smoothing parameters to the specific data and continuous re‐evaluation of accuracy and curvature is required. Depending on the number of vertices and the filter algorithm, computation time may vary strongly and interfere with an interactive mesh generation procedure. In this paper, we present an approach to improve the handling of mesh smoothing filters. Based on a GPU mesh smoothing implementation of uniform and anisotropic filters, model quality is evaluated in real‐time and provided to the user to support the mental optimization of input parameters. This is achieved by means of quality graphs and quality bars.en_US
dc.description.number8
dc.description.seriesinformationComputer Graphics Forumen_US
dc.description.volume32
dc.identifier.doi10.1111/cgf.12165en_US
dc.identifier.issn1467-8659en_US
dc.identifier.urihttps://doi.org/10.1111/cgf.12165en_US
dc.publisherThe Eurographics Association and Blackwell Publishing Ltd.en_US
dc.subjectGeometric modellingen_US
dc.subjectInteractionen_US
dc.subjectGPUs and their application for general purpose computingen_US
dc.subjectI.3.5 [Computer Graphics]en_US
dc.subjectComputational Geometry and Object Modelling Curveen_US
dc.subjectsurfaceen_US
dc.subjectsoliden_US
dc.subjectobject representationsen_US
dc.titleInteractive Mesh Smoothing for Medical Applicationsen_US
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