ScanIP

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ScanIP

ScanIP image processing software
Developer(s) Simpleware Ltd.
Operating system Windows
Website ScanIP homepage

ScanIP is an image processing software package developed by Simpleware Ltd. ScanIP visualises and processes 3D image data from MRI, CT, or Microtomography and other modalities to create simulation-ready CAD, CFD, FE, and 3D printing models.[1] Data can be visualised in 3D and 2D modes after import, and can be processed using powerful segmentation tools and filters to obtain regions of interest and optimise image quality. Measurement tools and a customisable statistics framework can also be used to quantify image data. Segmented images can be exported as surface models/meshes for computer-aided design and 3D printing or, with bolt-on module +FE, exported as surface/volume meshes directly into leading computational fluid dynamics and finite element packages using proprietary image-based meshing algorithms.[2] Further bolt-on modules +CAD and +NURBS can be used to integrate CAD objects into image data, and to fit NURBS patches to image data for CAD export. The most recent release of Simpleware ScanIP was 6.0, which launched in July 2013.

Import formats

  • DICOM
  • ACR-NEMA
  • Interfile
  • Analyze
  • MetaImage
  • Raw image data (binary, CSV...)
  • 2D images (jpg, tif...)

Export formats

Software features

User Interface & Visualisation

  • 2D & 3D visualisation
  • Layout customisation
  • 3D stereoscopic visualisation
  • Wireframe visualisation
  • Fast 3D preview
  • Transparent masks
  • Model shading
  • Macro recording
  • Colour and opacity mapping from histogram

Image Processing

  • Cropping
  • Rescaling
  • Resampling
  • Metal artefact reduction
  • Proprietary model smoothing algorithms
  • Topology and geometry preservation
  • Morphological filters
  • Level set methods using floodfilling to prevent poor contrast
  • Cavity fills
  • Island recovery
  • Noise filtering
  • Binarisation
  • Curvature flow
  • Skeletonisation

Segmentation

  • Thresholding
  • Floodfill
  • 3D editing
  • Magnetic lasso
  • Region growing
  • Fast segmentation of low contrast image data
  • Painting/unpainting
  • Multilevel Otsu segmentation
  • Automatic mask generation

Measurement & Statistics

  • Save points, distances and angles
  • Snap to 3D surface
  • Histogram creation
  • Profile line creation
  • Comma separated value exportation
  • Display and computation of statistics for individual regions of interest
  • Mask statistics
  • Model statistics
  • User defined statistics

Surface Model Generation

  • STLs for 3D printing or for further analysis and processing in CAD and other packages
  • Guaranteed watertight triangulations and correct norms
  • Conforming multi-parts and conforming interfaces
  • Laplacian-type smoothing on surface meshes prior to export
  • Coarsen or densify mesh to optimise surface quality

Bolt-on Modules

+FE: Volume and surface mesh generation

+FE generates volume meshes with conforming multi-parts for FEA and CFD. Contact sets and pairs, node sets and shell elements can also be added to meshes, while material properties can be assigned based on greyscale values. In addition, boundary conditions can be set, boundary layers meshed, and arbitrary inlets and outlets added for CFD analysis. Users can decide between a grid-based or a free meshing approach for optimising mesh quality, and can choose from all-tetrahedral or tetrahedral-hexahedral mesh options featuring linear or quadratic elements. Volume meshes generated using +FE provide an ideal extension of ScanIP's image processing capabilities for FE and CFD research, and are compatible with all leading solvers.

Export formats: ABAQUS (*.inp), ANSYS (*.ans), COMSOL Multiphysics (*.mphtxt), I-DEAS (*.unv), LS-DYNA (*.dyn), MSC (*.out), FLUENT (*.msh)

+CAD: Integration of CAD Models within Image Data

+CAD allows for the import and interactive positioning of CAD models within image data. The resulting combined models can then be exported as multi-part STLs or, using +FE, converted automatically into multi-part Finite Element or CFD meshes. Internal structures can also be added to data, which can be used for improving FEA accuracy, or for printing more lightweight 3D models. With +CAD, users can avoid having to work with image-based files in CAD-based software.

Import formats: Image data from ScanIP, IGES (*.iges, *.igs), STEP (*.step, *.stp), STL (*.stl)

Export formats: ScanIP files (for further processing), STL (*.stl)

+NURBS: Generation of CAD ready NURBS models

+NURBS allows segmented 3D image data to be fitted with Non-Uniform Rational B-Splines using automated patch fitting techniques for export as IGES files. Autosurface algorithms provide a straightforward route from image data to CAD-ready NURBS models, with options available for contour and curvature detection. CAD geometries can also be inspected prior to export to remove spurious features.

Export formats: IGES (*.iges)

Application Areas

ScanIP can be used to reconstruct complex anatomical geometries from 3D image data, generating computational models for simulating different biomechanical processes. Powerful filters and tools enable comprehensive medical image processing, identifying regions of interest (ROIs) and obtaining measurements and statistics based on greyscale data. Patient-specific image data can also be integrated with CAD-based implants within Simpleware’s +CAD module, and used for analysing fit and suitability, as well as vulnerability to different stresses and strains. Analysis can similarly be made of dental implants by integrating CAD objects with reconstructed patient data.[3][4] Some application areas for models created within Simpleware's software environment include forensic analysis, creating an anatomically accurate finite element mesh of a human head for different simulations,[5] simulating transcranial direct current stimulation,[6] and testing electrode placements for treating epilepsy.[7]

ScanIP has extensive applications to different materials science, rock physics, reverse engineering and nondestructive testing challenges. Scans of composites and materials samples can be easily visualised and processed in ScanIP, enabling multiple phases and porous networks to be identified using semi-automated thresholding and floodfill tools.[8][9] ScanIP can also be combined with +FE to generate volume meshes for FE and CFD characterisation of stress/strain distribution, porosity and other material properties.[10] Common applications include materials characterisation,[11] non-destructive testing and evaluation[12] and CFD analysis of rocks.[13]

ScanIP can be used to create computational models suitable for non-destructive evaluation and testing in CAD, FE and CFD solvers.[14] Scanned image data can be easily processed to identify regions of interest, with the potential to identify defects and cracks. Microstructures can also be generated for CAD inspection, and CAD objects integrated with scanned data to create robust models suitable for further meshing and export to solvers and for 3D printing. Applications include the reconstruction and characterisation of a range of material types, including composites,[15] foams,[16] and food.[17]

ScanIP has applications to reconstructing anatomies for the investigation of different biological processes. Paleontological uses of ScanIP include the reconstruction of dinosaur skeletons,[18] while more general morphological applications have included generating a surface model of a shark head suitable for rapid prototyping and testing of how sharks smell.[19] ScanIP has also been used for biomimicry projects for the Eden Project, and for producing artworks inspired by morphology.[20] Options are similarly available for generating high quality STL files from scans of the natural world, which can then be exported for 3D printing.

With ScanIP, it is possible to reverse engineer legacy parts and other geometries that cannot be accurately created in CAD. Scans of objects can be reconstructed and processed in ScanIP to learn more about their original design, and to explore parameters and material properties for FE and CFD simulation of design changes.[21] Moreover, reverse engineering of anatomies can enable researchers to learn more about biological processes.[22] More general applications include verifying product performance, permeability and stress-strain distribution.

ScanIP is capable of generating robust STL files for 3D printing applications. Models created using ScanIP feature guaranteed watertight triangulations and correct norms, as well as options for volume and topology preserving smoothing. STLs are generated with conforming interfaces, enabling multi-material printing. Internal structures can also be added to models to reduce material usage and weight for 3D printing.[23] General applications include the development of patient-specific implants, the reverse engineering of legacy parts, and the creation of high quality STL files for printing different objects derived from MRI, CT and other imaging modalities.

See also

References

  1. ↑ Johnson, E., Young, P., 2005. Simpleware: From 3D image to mesh in minutes. CSAR Focus, Edition 14 (Autumn - Winter 2005), 13-15. http://www.csar.cfs.ac.uk/about/csarfocus/focus14/focus14_simpleware.pdf
  2. ↑ Johnson, E., 2005. Simpleware: From 3D Image to Mesh. The Focus, Issue 39, 2.
  3. ↑ Queijo, L., Rocha, J., Barreira, L., Ramos, A., San Juan, M., Barbosa, T., 2009. Maxilla bone pre-surgical evaluation aided by 3D models obtained by Rapid Prototyping. Biodental Engineering, 139-144.
  4. ↑ Hohmann, A., Kober, C., Radtke, T., Young, P., Geiger, M., Boryor, A., Sander, C., Sander F.G., 2008. Feasibility study about finite element simulation of the dental periodontal ligament in vivo. Journal of Medical Biomechanics, 2008(01), 26-30.
  5. ↑ Head Analysis. Simpleware Case Studies. Simpleware Ltd. http://simpleware.com/industries/medical-and-dental/case-study-development-of-realistic-human-head-model.html
  6. ↑ Datta, A, Bikson M, Fregni F, (2010), Transcranial direct current stimulation in patients with skull defects and skull plates: High-resolution computational FEM study of factors altering cortical current flow. NeuroImage (52.4). pp. 1268-1278. http://dx.doi.org/10.1016/j.neuroimage.2010.04.252
  7. ↑ Rossi, M., Stebbins, G., Murphy, C., Greene, D, et al (2010) Predicting white matter targets for direct neurostimulation therapy. Epilepsy Research. Volume 91, Issues 2-3. pp. 176-186. http://dx.doi.org/10.1016/j.eplepsyres.2010.07.010
  8. ↑ Alghamdi, A., Khan, A., Mummery, P., & Sheikh, M., 2013. The characterisation and modelling of manufacturing porosity of a 2-D carbon/carbon composite. Journal of Composite Materials. http://jcm.sagepub.com/content/early/2013/09/13/0021998313502739.abstract
  9. ↑ Berre, C., Fok, S.L., Mummery, P.M., Ali, J., Marsden, B.J., Marrow, T.J., Neighbour, G.B., 2008. Failure analysis of the effects of porosity in thermally oxidised nuclear graphite using finite element modelling. Journal of Nuclear Materials, 381, 1–8.
  10. ↑ Coleri, E., & Harvey, J.T., 2013. A fully heterogeneous viscoelastic finite element model for full-scale accelerated pavement testing. Construction and Building Materials, 43, 14-30.
  11. ↑ Notarberardino, B., 2010. Image Based Finite Element Modelling for the Mechanical Characterisation of Complex Material Systems. Thesis (PhD), University of Exeter.
  12. ↑ Abdul-Aziz, A., Albumeri, G., Garg, M., Young, P.G., 2008. Structural testing of a Nickel based Superalloy Metal Foam via NDT and Finite Element Analysis. Materials Evaluation, 66(9), 949-954.
  13. ↑ Kaczmarczyk, J., Dohnalik, M., Zalewska, J., 2010. Three-dimensional Pore Scale Fluid Flow Simulation Based on Computed Microtomography Carbonate Rocks' Images. In: Pereira, J.C.F., Sequeira, A., Pereira, J.M.C. eds. Proceedings of the V European Conference on Computational Fluid Dynamics ECCOMAS CFD 2010, 14–17 June 2010 Lisbon.
  14. ↑ Węglewski, W., Bochenek, K., Basista, M., Schubert, Th., Jehring, U., Litniewski, J., & Mackiewicz, S., 2013. Comparative assessment of Young’s modulus measurements of metal-ceramic composites using mechanical and non-destructive tests and micro-CT based computational modeling. Computational Materials Science, 77, 19-30.
  15. ↑ Li, J., Li, H., Fok, A.S., Watts, D.C., 2012. Numerical evaluation of bulk material properties of dental composites using two-phase finite element models. Dental Materials, 28(9), 996-1003.
  16. ↑ Abdul-Aziz, A., Abumeri, G., Garg, M., Young, P.G., 2008. Structural Evaluation of a Nickel Base Super Alloy Metal Foam Via NDE and Finite Element. In: Smart Structures and Materials & Nondestructive Evaluation, 9–13 March 2008 San Diego. Bellingham: SPIE.
  17. ↑ Said, R., Schüller, R., Young, P., Aastveit, A., Egelandsdal, B., 2007. Simulation of salt diffusion in a pork (bacon) side using 3D imaging. In: Petit, J.-M., Squalli, O. eds. Proceedings of the European COMSOL Conference 2007, 23-24 October 2007 Grenoble. Grenoble: COMSOL France, Vol 2, 876-881.
  18. ↑ Manning, P.L., Margetts, L., Johnson, M.R., Withers, P.J., Sellers, W.I., Falkingham, P.L., Mummery, P.M., Barrett, P.M., Raymont, D.R., 2009. Biomechanics of Dromaeosaurid Dinosaur Claws: Application of X-Ray Microtomography, Nanoindentation, and Finite Element Analysis. The Anatomical Record: Advances in Integrative Anatomy and Evolutionary Biology, 292, 1397–1405.
  19. ↑ Abel, R.L., Maclaine, J.S., Cotton, R., Bui Xuan, V., Nickels, T.B., Clark, T.H., Wang, Z., Cox, J.P.L., 2010. Functional morphology of the nasal region of a hammerhead shark. Comparative Biochemistry and Physiology, Part A, 155, 464–475.
  20. ↑ Simpleware will contribute to Biomimicry display. CFDFea.com. 15th June 2005.http://www.cfdfea.com/2005/06/simpleware-joins-the-eden-project-in-public-awareness-scheme/
  21. ↑ Wang, W., & Genc, K., 2012. Multiphysics Software Applications in Reverse Engineering. In: COMSOL Conference 2012, 3–5 October 2012 Boston, USA.
  22. ↑ Nguyen, V.N., Lilly, B.W., & Castro, C.E., 2012. Reverse Engineering the Structure and Function of the Allegheny Mound Ant Neck. In: ASME 2012 International Mechanical Engineering Congress & Exposition, 9–15 November 2012 Houston, Texas, USA.
  23. ↑ Young, P., Raymont, D., Hao, L, Cotton, R., 2010. Internal Micro-Architecture Generation. In: TCT Additive Manufacturing Conference, 19–20 October 2010 Coventry.
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