Image-based meshing: Difference between revisions

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'''Image-based meshing''' is openingthe upautomated exciting new possibilities for the applicationprocess of computationalcreating continuumcomputer mechanicsmodels (numerical methods such asfor [[Computationalcomputational fluid dynamics]] (CFD) and [[Finite element method|Finitefinite Elementelement 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, (such as can be obtained from [[Magneticmagnetic resonance imaging]] (MRI), [[Computedcomputed tomography]] (CT) or [[Microtomographymicrotomography]]). for example,Although area increasinglywide in demand. Different methodsrange of generating[[mesh thegeneration]] requiredtechniques volumeare discretizationscurrently directlyavailable, andthese robustlywere from the image data have beenusually developed, howeverto theregenerate aremodels afrom range[[computer-aided ofdesign]] issues related to image processing(CAD), and meshtherefore generationhave whichdifficulties stillmeshing needfrom to3D beimaging addresseddata.

==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 a more realistic and accurate geometrical description of the computational ___domain. TheThere 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 theare complexgenerally topologiestwo typicalways 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 surface3D imaging data. Image-based mesh generation raises a number of issues which are different from CAD-based model generation:

===CAD-based approach===

The majority of approaches used to date still follow the traditional CAD route by using an intermediary step of surface reconstruction which is then followed by a traditional CAD-based meshing algorithm.<ref>Viceconti et al, 1998. TRI2SOLID: an application of reverse engineering methods to the creation of CAD models of bone segments. Computer Methods and Programs in Biomedicine, 56, 211–220.</ref> CAD-based approaches use the scan data to define the surface of the ___domain and then create elements within this defined boundary. Although reasonably robust algorithms are now available, these techniques are often time consuming, and virtually intractable for the complex topologies typical of image data. They also do not easily allow for more than one ___domain to be meshed, as multiple surfaces are often non-conforming with gaps or overlaps at interfaces where one or more structures meet.<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&ndash;3173.</ref>

===Image-based approach===

This approach is the more direct way as it combines the geometric detection and mesh creation stages in one process. Thewhich techniqueoffers hasa beenmore pioneered by Simpleware,robust and generatesaccurate 3Dresult hexahedralthan ormeshing tetrahedralfrom elementssurface throughoutdata. theVoxel volumeconversion oftechnique theproviding ___domain,meshes thuswith creatingbrick theelements mesh<ref>Fyhrie directlyet with conforming multipartal, surfaces1993. InThe theprobability casedistribution of modelingtrabecular complexlevel topologiesstrains withfor possiblyvertebral hundredscancellous bone. Transactions of disconnectedthe domains39th (e.g.Annual inclusionsMeeting inof athe matrix)Orthopaedic Research Society, approachingSan theFrancisco.</ref> problemand 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 Journal of Methods in Engineering'', robust37, accurate2735–2753.</ref> andhave been efficientproposed.

Another approach generates 3D tetrahedral 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-&ndash;3173.</ref>

==Generating a model==

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===Scan and image processing===

An extensive range of [[Imageimage processing]] tools can be used to generate highly accurate models based on data from 3D imaging modalities, e.g. MRI, CT, MicroCT (XMT), and Ultrasound. Features of particular interest include:

* [[Segmentation (image processing)|Segmentation tools]] (e.g. Thesholdingthresholding, Floodfillfloodfill, Levellevel set methods, etc.)

* [[Smoothing|Filters and Smoothingsmoothing tools]] (e.g. Volumevolume- and topology -preserving smoothing and noise reduction/artefact removing).

===Volume and surface mesh generation===

The Imageimage-based meshing technique allows the straightforward generation of meshes out of segmented 3D data. Features of particular interest include:

* Multi-part meshing (mesh any number of structures simultaneously)

* Mapping functions to apply material properties based on signal strength (e.g. [[Young's modulus]] to [[Hounsfield scale]])

* Smoothing of meshes (e.g. topological preservation of data to ensure preservation of connectivity, and volume neutral smoothing to prevent shrinkage of convex hulls)

* Export to FEA and CFD codes for analysis (e.g. nodesnode sets, shell elements, material properties, contact surfaces, boundary layers, inlets/outlets)

==Typical use==

* [[Biomechanics]] and design of [[Implant (medicine)|Medicalmedical and dental implants]]

* [[Food science]]

* [[Forensic science]]

* [[Materials science]] (composites and foams)

* [[Nondestructive testing]] (NDT)

* [[Paleontology]] and [[Morphology (biology)|Functionalfunctional morphology]]

* [[Reverse engineering]]

* [[Soil science]] and [[Petrology]]

* [[Petrophysics]]

==PublicationsSee also==

*[[Image segmentation]]

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.

==References==

<references/>

==External links==

* [https://www.computing-objects.com/ Computing-Objects] commercial C++ libraries for mesh generation & FEM computation

Simpleware* Ltd.[[ScanIP]] commercial image-based meshing software: ([http://www.simpleware.com www.simpleware.com])

* Mimics 3D image-based engineering software for FEA and CFD on anatomical data: [http://www.materialise.com/mimics Mimics website] {{Webarchive|url=https://web.archive.org/web/20110212150457/http://www.materialise.com/mimics |date=2011-02-12 }}

* Google group on image-based modelling: [http://groups.google.co.uk/group/image-based-modelling]

* [[Avizo (software)|Avizo Software]]'s 3D image-based meshing tools for CFD and FEA

* iso2mesh: a free 3D surface and volumetric mesh generator for matlab/octave [https://iso2mesh.sourceforge.net/]

* [http://www.ctcms.nist.gov/oof/ OOF3D], object oriented finite element analysis from the [[NIST]]

* [https://www.volumegraphics.com/en/products/vgstudio-max/add-on-modules-for-simulation.html VGSTUDIO MAX], Commercial CT analysis software for industry. They offer an add-on module for FEM meshing.

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[[Category:Mesh generation]]

[[Category:Computer graphics algorithms]]

[[Category:3D computer graphics]]