'''Image-based meshing''' isit openingthe upautomated exciting new possibilities for the applicationprocess of computationalcreating continuumcomputer mechanicsmodels (numerical methods such asfor [[Computational fluid dynamics]] (CFD) and [[Finite element method|Finite Element analysis]] to problems in [[Biomechanics]], [[Soil mechanics]], [[Characterization ( materials scienceFEA) |Material characterization]], and [[Nondestructive testing]]. Meshing techniques that can rapidly generate robust, high quality meshes from complex 3D image data, as can be obtained from [[Magnetic resonance imaging]] (MRI), [[Computed tomography]] (CT) or [[Microtomography]] for example, are increasingly in demand. Different methods of generating the required volume discretizations directly and robustly from the image data have been developed, howeverand there arealthough a wide range of issuesmesh relatedgeneration totechniques imageare processingcurrently andavailable, meshthese generationon whichthe stillwhole, needhave tonot bebeen developed with meshing from segmented 3D imaging data in addressedmind. ▼{{coi|date=August 2009}}
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▲'''Image-based meshing''' is opening up exciting new possibilities for the application of computational continuum mechanics (numerical methods such as [[Computational fluid dynamics]] (CFD) and [[Finite element method|Finite Element analysis]] to problems in [[Biomechanics]], [[Soil mechanics]], [[Characterization (materials science)|Material characterization]], and [[Nondestructive testing]]. Meshing techniques that can rapidly generate robust, high quality meshes from complex 3D image data, as can be obtained from [[Magnetic resonance imaging]] (MRI), [[Computed tomography]] (CT) or [[Microtomography]] for example, are increasingly in demand. Different methods of generating the required volume discretizations directly and robustly from the image data have been developed, however there are a range of issues related to image processing and mesh generation which still need to be addressed.
==Mesh generation from 3D imaging data==
Although a wide range of mesh generation techniques are currently available these, on the whole, have not been developed with meshing from segmented 3D imaging data in mind. Meshing from 3D imaging data presents a number of challenges but also unique opportunities for presenting more realistic and accurate geometrical description of the computational ___domain. The majority of approaches adopted have involved generating a surface model (either in a discretized or continuous format) from the scan data, which is then exported to a commercial mesher – so-called ‘CAD-based approach’. This process is often time consuming, not very robust and virtually intractable for the complex topologies typical of image data. A more direct way is the ‘Image-based approach’ as it combines the geometric detection and mesh creation stages in one process which offers a more robust and accurate result than meshing from surface data. Image-based mesh generation raises a number of issues which are different from CAD-based model generation:
===CAD-based approach===
===Image-based approach===
This approach combines the geometric detection and mesh creation stages in one meshing process. The techniquemost hascommonly beenapplied pioneeredmeshing byprocedures Simpleware,are andthe generatesvoxel 3Dconversion hexahedraltechnique orproviding tetrahedralmeshes elementswith throughoutbrick theelements volume<ref>Fyhrie ofet the ___domainal, thus1993. creatingThe theprobability meshdistribution directlyof withtrabecular conforminglevel multipartstrains surfaces.for Invertebral thecancellous casebone. Transactions of modelingthe complex39th topologiesAnnual withMeeting possiblyof hundredsthe ofOrthopaedic disconnectedResearch domainsSociety, (e.gSan Francisco.</ref> inclusionsand inthe amarching matrix),cube approachingalgorithm theproviding problemmeshes viawith atetrahedral CAD-basedelements approach<ref>Frey iset virtuallyal, intractable1994. ByFully contrastautomatic treatingmesh thegeneration problemfor using3-D andomains Image-based approachupon isvoxel remarkablysets. straightforward,''International robust,Journal accurateof andMethods efficientin Engineering'', 37, 2735–2753.</ref>).
The technique generates 3D hexahedral or tetrahedral elements throughout the volume of the ___domain, thus creating the mesh directly with conforming multipart surfaces<ref>Young et al, 2008. An efficient approach to converting 3D image data into highly accurate computational models. ''Philosophical Transactions of the Royal Society A'', 366, 3155-3173.</ref>. In the case of modeling complex topologies with possibly hundreds of disconnected domains (e.g. inclusions in a matrix), approaching the problem via a CAD-based approach is virtually intractable. By contrast treating the problem using an Image-based approach is remarkably straightforward, robust, accurate and efficient.
==Generating a model==
* [[Soil science]] and [[Petrology]]
==PublicationsReferences==
<references/>
Young et al, 2008. An efficient approach to converting 3D image data into highly accurate computational models. Philosophical Transactions of the Royal Society A, 366, 3155-3173.
==External links==
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