Skip to main content
SpringerLink
Log in

User Interfaces to Interact with Tensor Fields

  • Chapter
Tensors in Image Processing and Computer Vision

Part of the book series: Advances in Pattern Recognition ((ACVPR))

  • 2743 Accesses

Abstract

Nowadays there is a growing interest in tensor medical imaging modalities. In Diffusion Tensor Magnetic Resonance Imaging (DT-MRI), each pixel is valued with a symmetric second-order tensor describing the spatial properties of diffusion at that point. Therefore, it provides significantly more information than scalar modalities, but this causes the complexity of the interfaces dealing with them to grow. In this chapter, the current situation of user interfaces for tensor fields is reviewed. Tensor user interfaces are difficult to design, given the difficulty of mentally integrating data with so many parameters. This is why a considerable effort must be invested in order to achieve intuitive and easy-to-use interfaces. The display of tensor information plays an important role in this, and we review several existing visualization methods for tensor fields.We must point out that, although most of the applications are graphical interfaces, there are also examples of command-line tools and multimodal interfaces employing virtual environments. We study some of the urrent medical user interfaces for diffusion tensor fields.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Aksoy, M., Avcu, N., Merino-Caviedes, S., Diktaş, E.D., Martín-Fernández, M.A., Girgin, S., Marras, I., Muñoz Moreno, E., Tekeli, E., Acar, B., Bammer, R., Martin-Fernández, M., Sahiner, A.V., Üskudarli, S.: DTI Application with Haptic Interfaces. In: Proc. eNTERFACE’07 Workshop Multimodal Interfaces, pp. 1–9. Istanbul, Turkey (2007)

    Google Scholar 

  2. Arsigny, V., Fillard, P., Pennec, X., Ayache, N.: Log-Euclidean Metrics for Fast and Simple Calculus on Diffusion Tensors. Magn. Reson. Med. 56(2), 411–421 (2006)

    Article  Google Scholar 

  3. Basser, P.J., Jones, D.K.: Diffusion-tensor MRI: theory, experimental design and data analysis–a technical review. NMR Biomed. 15(7–8), 456–467 (2002)

    Article  Google Scholar 

  4. Basser, P.J., Pierpaoli, C.: Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J. Magn. Reson. Ser. B 111, 209–219 (1996)

    Article  Google Scholar 

  5. Bihan, D.L., Mangin, J., Poupon, C.l., Clark, C.A., Pappata, S., Molko, N., Chabriat, H.: Diffusion tensor imaging: concepts and applications. J. Magn. Reson. Imaging 13(4), 534–546 (2001)

    Article  Google Scholar 

  6. Brun, A., Park, H., Knutsson, H., Westin, C.: Coloring of DT-MRI fiber traces using Laplacian eigenmaps. In: Proc. 9th Int. Conf. Comput. Aided Syst. Theory (EUROCAST), pp. 564–572. Las Palmas de Gran Canaria, Spain (2003)

    Google Scholar 

  7. Burrell, A., Sodan, A.: Web Interface Navigation Design: Which Style of Navigation-Link Menus Do Users Prefer? In: Proc. 22nd Int. Conf. Data Eng. Workshops, pp. 1–10. Atlanta, GA, USA (2006)

    Google Scholar 

  8. Cho, S., Oh, J.K., Bang, W., Chang, W., Choi, E., Jing, Y., Cho, J., Kim, D.Y.: Magic wand: a hand-drawn gesture input device in 3-D space with inertial sensors. In: Frontiers in Handwriting Recognition, 2004. IWFHR-9 2004. Ninth International Workshop on, pp. 106–111. Kokubunji, Tokyo, Japan (2004)

    Google Scholar 

  9. Cook, P.A., Bai, Y., Nedjati-Gilani, S., Seunarine, K.K., Hall, M.G., Parker, G.J., Alexander, D.C.: Camino: Open-Source Diffusion-MRI Reconstruction and Processing. In: 14th Sci. Meet. Int. Soc. Magn. Reson. Med., p. 2759. Seattle, WA, USA (2006)

    Google Scholar 

  10. Cruz-Neira, C., Sandin, D.J., DeFanti, T.A., Kenyon, R.V., Hart, J.C.: The CAVE: audio visual experience automatic virtual environment. Commun. ACM 35(6), 64–72 (1992)

    Article  Google Scholar 

  11. Delmarcelle, T., Hesselink, L.: Visualizing Second-Order Tensor Fields with Hyperstreamlines. IEEE Comput. Graphics Appl. 13(4), 25–33 (1993)

    Article  Google Scholar 

  12. Ehricke, H.H., Klose, U., Grodd, W.: Visualizing MR diffusion tensor fields by dynamic fiber tracking and uncertainty mapping. Comput. & Graphics 30, 255–264 (2006)

    Article  Google Scholar 

  13. Electron. Resour.: 3D Slicer. Medical Visualization and Processing Environment for Research.www.slicer.org. Online: June 2006

  14. Electron. Resour.: Analyze 7.0. New Features/Enhancements. http://www.analyzedirect.com/Analyze/enhancements.asp. Online: November 2006.

  15. Electron. Resour.: BioTensor Tutorial. http://software.sci.utah.edu/doc/User/Tutorials/BioTensor/BioTensor.htm l. Online: July 2006

  16. Electron. Resour.: Brigham and Women’s Hospital website. http://www.brighamandwomens.org/. Online: June 2006

  17. Electron. Resour.: Computer Science and Artificial Intelligence Laboratory. http://www.csail.mit.edu/index.php. Online: June 2006

  18. Electron. Resour.: GNU Operating System. Licenses. http://www.gnu.org/licenses/licenses.html. Online: August 2008

  19. Electron. Resour.: MedINRIA. Asclepios Research Project –- INRIA Sophia Antipolis. http://www-sop.inria.fr/asclepios/software/MedINRIA/. Online: November 2006

  20. Electron. Resour.: National Alliance for Medical Image Computing. http://www.na-mic.org. Online: June 2006

  21. Electron. Resour.: Nearly Raw Raster Data. http://teem.sourceforge.net/nrrd/index.html. Online: December 2006

  22. Electron. Resour.: Software. http://arnaud.guidon.free.fr/blog/?page_id=4. Online: November 2006

  23. Electron. Resour.: Teem Summary. http://www.na-mic.org/Wiki/index.php/TeemSummary. Online: November 2006.

  24. Electron. Resour.: wxWidgets. Cross-platform GUI Library. http://www.wxwidgets.org. Online: September 2008.

  25. Gering, D., Nabavi, A., Kikinis, R., Eric, W., Grimson, L., Hata, N., Everett, P., Jolesz, F., Wells III, W.: An Integrated Visualization System for Surgical Planning and Guidance using Image Fusion and Interventional Imaging. In: Med. Image Comput. Comput.-Assist. Interv. (MICCAI) (1999)

    Google Scholar 

  26. Gering, D.T.: A System for Surgical Planning and Guidance using Image Fusion and Interventional MR. Master’s thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology (1999)

    Google Scholar 

  27. Goldberg-Zimring, D., Mewes, A.U.J., Maddah, M., Warfield, S.K.: Diffusion Tensor Magnetic Resonance Imaging in Multiple Sclerosis. J. Neuroimaging 15(s4), 68S–81S (2005)

    Article  Google Scholar 

  28. Ibanez, L., Schroeder, W., Ng, L., Cates, J.: The ITK Software Guide. http://www.itk.org/ItkSoftwareGuide.pdf, second ed. (2005)

  29. Jiang, H., van Zijl, P.C.M., Kim, J., Pearlson, G.D., Mori, S.: Dtistudio: Resource program for diffusion tensor computation and fiber bundle tracking. Comput. Methods Programs Biomed. 81(2), 106–116 (2006)

    Article  Google Scholar 

  30. Kindlmann, G.: Superquadric Tensor Glyphs. In: Jt. EUROGRAPHICS–IEEE TCVG Symp. Vis., pp. 147–154, (2004)

    Google Scholar 

  31. Kindlmann, G., Weinstein, D., Hart, D.: Strategies for Direct Volume Rendering of Diffusion Tensor Fields. IEEE Trans. Vis. Comput. Graphics 6(2), 124–138 (2000)

    Article  Google Scholar 

  32. Kingsley, P.B.: Introduction to diffusion tensor imaging mathematics: Part I. Tensors, rotations, and eigenvectors. Concepts Magn. Reson. Part A 28A(2), 101–122 (2006)

    Article  Google Scholar 

  33. Kingsley, P.B.: Introduction to diffusion tensor imaging mathematics: Part II. Anisotropy, diffusion-weighting factors, and gradient encoding schemes. Concepts Magn. Reson. Part A 28A(2), 123–154 (2006)

    Article  Google Scholar 

  34. Kitware, Inc.: KWWidgets. http://www.kwwidgets.org/Wiki/KWWidgets. Online: september 2008

  35. Kjeldskov, J.: Interaction: Full and partial Immersive Virtual Reality Displays. In: Proc. IRIS24, pp. 587–600. Bergen, Norway (2001)

    Google Scholar 

  36. Kubicki, M., Westin, C.F., McCarley, R.W., Shenton, M.E.: The Application of DTI to Investigate White Matter Abnormalities in Schizophrenia. Ann. N. Y. Acad. Sci. 1064, 134–148 (2005)

    Article  Google Scholar 

  37. Laidlaw, D.H., Ahrens, E.T., Kremers, D., Avalos, M.J., Jacobs, R.E., Readhead, C.: Visualizing diffusion tensor images of the mouse spinal cord. In: Proc. Vis. ’98., pp. 127–134, 527. Research Triangle Park, NC, USA (1998)

    Google Scholar 

  38. Melhem, E.R., Mori, S., Mukundan, G., Kraut, M.A., Pomper, M.G., van Zijl, P.C.M.: Diffusion Tensor MR Imaging of the Brain and White Matter Tractography. Am. J. Roentgenol. 178(1), 3–16 (2002)

    Google Scholar 

  39. Papademetris, X., Jackowski, M., Rajeevan, N., Constable, R.T., Staib, L.H.: BioImage Suite: An integrated medical image analysis suite. http://www.bioimagesuite.org/. Online: september 2008.

  40. Pieper, S.D., Halle, M., Kikinis, R.: 3D Slicer. In: Proc. 2004 IEEE Int. Symp. Biomed. Imaging: From Nano to Macro, pp. 632–635. Arlington, VA, USA (2004)

    Google Scholar 

  41. Schroeder, W., Martin, K., Lorensen, B.: The Visualization Toolkit. An Object Oriented Approach to 3D Graphics. Kitware, Inc., New Jersey (2002)

    Google Scholar 

  42. Sundgren, P.C., Dong, Q., Gómez-Hassan, D., Mukherji, S.K., Maly, P., Welsh, R.: Diffusion tensor imaging of the brain: review of clinical applications. Neuroradiol. 46, 339–350 (2004)

    Article  Google Scholar 

  43. Talos, I.F., O’Donell, L., Westin, C.F., Warfield, S.K., Wells III, W., Yoo: Diffusion Tensor and Functional MRI Fusion with Anatomical MRI for Image Guided Neurosurgery. In: Proc. 6th Int. Conf. Med. Image Comput. Comput.-Assist. Interv. (MICCAI 2003). Montr é al, Canada (2003)

    Google Scholar 

  44. Weinstein, D., Kindlmann, G., Lundberg, E.: Tensorlines: Advection-Diffusion based Propagation through Diffusion Tensor Fields. In: IEEE Vis. ’99, pp. 249–253. San Francisco, CA, USA (1999)

    Google Scholar 

  45. Welch, B.B.: Practical Programming in Tcl and Tk. Prentice Hall PTR, New Jersey (1997)

    Google Scholar 

  46. Westin, C.F., Maier, S., Mamata, H., Nabavi, A., Jolesz, F.A., Kikinis, R.: Processing and visualization for diffusion tensor MRI. Med. Image Anal. 6, 93–108 (2002)

    Article  Google Scholar 

  47. W ü nsche, B.C.: A Toolkit for the Visualization of Tensor Fields in Biomedical Finite Element Models. Ph.D. thesis, Department of Computer Science. University of Auckland (2004)

    Google Scholar 

  48. Zhang, S., Demiralp, c., Keefe, D.F., DaSilva, M., Laidlaw, D.H., Greenberg, B.D., Basser, P.J., Pierpaoli, C., Chiocca, E.A., Deisboeck, T.S.: An immersive virtual environment for DT-MRI volume visualization applications: a case study. In: Proc.Conf. Vis. ’01, VIS ’01, pp. 437–440. IEEE Computer Society, Washington DC, WA, USA (2001)

    Google Scholar 

  49. Zhang, S., Demiralp, c., Laidlaw, D.H.: Visualizing Diffusion Tensor MR Images Using Streamtubes and Streamsurfaces. IEEE Trans. Vis. Comput. Graphics 9(4), 454–462 (2003)

    Article  Google Scholar 

  50. Zheng, X., Pang, A.: Volume Deformation For Tensor Visualization. In: IEEE Vis. 2002, pp. 379–386. Boston, MA, USA (2002)

    Google Scholar 

  51. Zheng, X., Pang, A.: HyperLIC. In: IEEE Vis. 2003, pp. 249–256. Seattle, WA, USA (2003)

    Google Scholar 

  52. Zheng, X., Parlett, B.N., Pang, A.: Topological Lines in 3D Tensor Fields and Discriminant Hessian Factorization. IEEE Transactions on Visualization and Computer Graphics 11(4), 395–407 (2005)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory of Image Processing, University of Valladolid, Spain

    Susana Merino-Caviedes & Marcos Martín-Fernández

Authors
  1. Susana Merino-Caviedes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marcos Martín-Fernández
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Susana Merino-Caviedes .

Editor information

Editors and Affiliations

  1. Escuela Técnica Superior de Ingenieros de Telecomunicacíon, Universidad de Valladolid, Campus Miguel Delibes, 47011 Valladolid, Spain

    Santiago Aja-Fernández  & Rodrigo de Luis García  & 

  2. Dept. Computing, HongKong Polytechnic University, Hong Kong

    Dacheng Tao

  3. University of London Birkbeck College School of Computer Science & Information Systems, Malet Street, London, WC1E 7HX, UK

    Xuelong Li

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag London Limited

About this chapter

Cite this chapter

Merino-Caviedes, S., Martín-Fernández, M. (2009). User Interfaces to Interact with Tensor Fields. In: Aja-Fernández, S., de Luis García, R., Tao, D., Li, X. (eds) Tensors in Image Processing and Computer Vision. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-299-3_20

Download citation

  • DOI: https://doi.org/10.1007/978-1-84882-299-3_20

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-298-6

  • Online ISBN: 978-1-84882-299-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation